Highly Efficient, Massively-Parallel Single-Cell RNA-Seq Reveals Cellular States and Molecular Features of Human Skin Pathology
bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.
Highly Efficient, Massively-Parallel Single-Cell RNA-Seq Reveals Cellular States and Molecular Features of Human Skin Pathology
Travis K Hughes1,2,3,4,5,9, Marc H Wadsworth II1,3,4,5,9, Todd M Gierahn5,6,9, Tran Do7, David Weiss7, Priscilla R. Andrade7, Feiyang Ma7, Bruno J. de Andrade Silva7, Shuai Shao8, Lam C Tsoi8, Jose Ordovas-Montanes1,3,4,5, Johann E Gudjonsson8, Robert L Modlin9, J Christopher Love3,4,5,6,10, and Alex K Shalek1,2,3,4,5,10
Affiliations:
1 Institute for Medical Engineering & Science (IMES) and Department of Chemistry, MIT,
Cambridge, Massachusetts, USA
2 Department of Immunology, Harvard Medical School, Boston, MA 02115, USA
3 Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
4 Ragon Institute of MGH, MIT and Harvard, Cambridge, Massachusetts, USA
5 Koch Institute for Integrative Cancer Research, MIT, Cambridge, Massachusetts, USA
6 Department of Chemical Engineering, MIT, Cambridge, Massachusetts, USA
7 Division of Dermatology and Department of Microbiology, Immunology and Molecular Biology, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
8 Department of Dermatology, University of Michigan, Ann Arbor, Michigan, USA
9 These authors contributed equally to this work
10 These senior authors contributed equally to this work
10 To whom correspondence should be addressed: [email protected] (AKS), [email protected] (JCL)
bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.
1 SUMMARY
2 The development of high-throughput single-cell RNA-sequencing (scRNA-Seq) methodologies 3 has empowered the characterization of complex biological samples by dramatically increasing the 4 number of constituent cells that can be examined concurrently. Nevertheless, these approaches 5 typically recover substantially less information per-cell as compared to lower-throughput microtiter 6 plate-based strategies. To uncover critical phenotypic differences among cells and effectively link 7 scRNA-Seq observations to legacy datasets, reliable detection of phenotype-defining transcripts 8 – such as transcription factors, affinity receptors, and signaling molecules – by these methods is 9 essential. Here, we describe a substantially improved massively-parallel scRNA-Seq protocol we 10 term Seq-Well S^3 (“Second-Strand Synthesis”) that increases the efficiency of transcript capture 11 and gene detection by up to 10- and 5-fold, respectively, relative to previous iterations, surpassing 12 best-in-class commercial analogs. We first characterized the performance of Seq-Well S^3 in cell 13 lines and PBMCs, and then examined five different inflammatory skin diseases, illustrative of 14 distinct types of inflammation, to explore the breadth of potential immune and parenchymal cell 15 states. Our work presents an essential methodological advance as well as a valuable resource 16 for studying the cellular and molecular features that inform human skin inflammation. bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.
17 INTRODUCTION
18 Although a nascent technology, single-cell RNA-sequencing (scRNA-Seq) has already
19 helped define, at unprecedented resolution, the cellular composition of many healthy and
20 diseased tissues (Klein et al., 2015; Macosko et al., 2015; Montoro et al., 2018; Ordovas-
21 Montanes et al., 2018; Vento-Tormo et al., 2018). The development of high-throughput
22 methodologies has been crucial to this process, empowering the characterization of increasingly
23 complex cellular samples. Unfortunately, current scRNA-Seq platforms typically demonstrate an
24 inverse relationship between the number of cells that can be profiled at once and the amount of
25 biological information that can be recovered from each cell. As a result, one must choose between
26 quantity and quality – and thus comprehensiveness and fidelity – or alternatively employ two
27 distinct approaches in parallel (Tabula Muris Consortium et al., 2018). Indeed, inefficiencies in
28 transcript capture among massively-parallel methods have limited our ability to resolve the distinct
29 cell states that comprise broad cell types (Braga et al., 2019), as well as their essential molecular
30 attributes and often lowly-expressed molecular features, such as transcription factors, affinity
31 receptors, and signaling molecules (Figure 1A).
32 Improving the fidelity of these methodologies is particularly important for resolving
33 differences within heterogeneous populations of immune cells like lymphocytes and myeloid cells
34 (Villani et al., 2017). Here, subtle differences in surface receptor, transcription factor and/or
35 cytokine expression can profoundly impact cellular function, particularly in the setting of human
36 pathology (Puel et al., 1998). Enhancing data quality in high-throughput scRNA-Seq would
37 facilitate a greater appreciation of the underlying molecular features that describe such cellular
38 variation. Similarly, it would ease integration with legacy datasets that often rely on lowly-
39 expressed biomarkers, such as transcription factors, that are false-negative prone to discriminate
40 subsets of cells. bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.
41 Most high-throughput scRNA-Seq methods currently rely on early barcoding of cellular
42 contents to achieve scale. Typically, these techniques recover single-cell transcriptomes for
43 thousands of cells at once by leveraging reverse-emulsion droplets or microwells to isolate
44 individual cells with uniquely barcoded poly-dT oligonucleotides which can then capture and tag
45 cellular mRNAs during reverse transcription (Prakadan et al., 2017). Afterward, an additional
46 priming site is added to the 3’ end of the synthesized cDNA to enable PCR-based amplification
47 of all transcripts using a single primer (whole transcriptome amplification, WTA). A number of
48 techniques have been described to add this second priming site (Sasagawa et al., 2013; Shishkin
49 et al., 2015). The most common uses the terminal transferase activity of certain reverse
50 transcription enzymes to facilitate a “template-switch” from the original mRNA to a second defined
51 oligonucleotide handle (Picelli et al., 2013). While simple to implement, this process has the
52 potential to be highly inefficient, leading to the loss of molecules that have been captured and
53 converted to cDNA but not successfully tagged with a secondary PCR priming site (Figure 1A
54 and S1A) (Islam et al., 2012; Kapteyn et al., 2010; Zajac et al., 2013).
55 To overcome these limitations, we have developed a new massively-parallel scRNA-Seq
56 protocol we call Seq-Well S^3 (for “Second-Strand Synthesis”). Seq-Well S^3 increases the
57 efficiency of the second PCR handle addition by amending it through a randomly-primed second-
58 strand synthesis after reverse transcription (Figure 1A). Working with cell lines and peripheral
59 blood mononuclear cells (PBMCs), we demonstrate that Seq-Well S^3 enables significant
60 improvements in transcript and gene capture across sample types, facilitating studies of complex
61 immune tissues at enhanced resolution (Figures 1, S1, and S2).
62 To illustrate the utility of S^3, we apply it to generate a resource of single-cell
63 transcriptional states spanning multiple inflammatory skin conditions. Skin represents the largest
64 barrier tissue in the human body and is comprised of numerous specialized cell-types that help
65 maintain both immunological and physical boundaries between our inner and outer worlds bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.
66 (Kabashima et al., 2019). The dermis and epidermis – the two primary compartments of human
67 skin – play complementary roles in tissue structure and function (Figure 2A) (Kabashima et al.,
68 2019). The epidermis consists primarily of keratinized epithelial cells, which provide a physical
69 barrier to the outside world; the dermis, meanwhile, provides structural support for the skin, with
70 fibroblasts producing collagen and elastin fibrils along with the other components of the
71 extracellular matrix. Crucially, within the cellular ecosystem of human skin, there are numerous
72 tissue-resident immune and parenchymal cells essential to homeostatic barrier function. Using
73 Seq-Well S^3, we examine the cellular composition of normal skin and altered cellular phenotypes
74 in multiple inflammatory skin conditions, including acne, alopecia areata, granuloma annulare,
75 leprosy and psoriasis. With conditions that span autoimmune (alopecia), autoinflammatory
76 (psoriasis), reactive (acne), and granulomatous (granuloma annulare and leprosy) inflammation,
77 we uncover a diverse spectrum of immune and parenchymal cellular phenotypes, as well as their
78 molecular features, across multiple inflammatory skin conditions. Overall, our work presents an
79 essential methodological advance as well as a critical resource for understanding how diverse
80 inflammatory responses can impact a single tissue and the range of cellular phenotypes that are
81 possible upon perturbation.
82
83 RESULTS
84 Second-Strand Synthesis (S^3) Leads to Improved Transcript Capture and Gene Detection
85 We hypothesized that use of “template-switching” to append a second PCR handle during
86 reverse transcription might limit the overall recovery of unique transcripts and genes from
87 individual cells in some massively-parallel scRNA-Seq methods such as Seq-Well and Drop-Seq
88 (Gierahn et al., 2017; Macosko et al., 2015). Thus, we incorporated a randomly primed second-
89 strand synthesis following first-strand cDNA construction (Figures 1A and S1A). Briefly, after
90 reverse transcription, barcoded mRNA capture beads are washed with 0.1 molar sodium bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.
91 hydroxide to remove attached RNA template strands and then a random second-strand synthesis
92 is performed to generate double-stranded cDNA labeled on one end with the SMART sequence
93 and its reverse complement on the other (Figure 1A and S1A) (Picelli et al., 2013, 2014).
94 To examine the effectiveness of Seq-Well S^3 and optimize its performance, we first
95 tested a number of conditions using cell lines (Figure S1B). In these experiments, we observed
96 that S^3 led to marked improvements in library complexity (Seq-Well V1: 0.22 transcripts/ aligned
97 read, Seq-Well S^3: 0.68 transcripts/ aligned read) and was able to function in the absence of a
98 template switching oligo (TSO); Seq-Well V1, meanwhile, failed to generate appreciable product
99 without a TSO (Figure S1B-D). In species-mixing experiments using HEK293 (human) and NIH-
100 3T3 (mouse) cell lines, the use of the S^3 protocol resulted in significant increases in the numbers
101 of unique transcripts captured and genes detected per cell compared to our original protocol for
102 Seq-Well (P < 0.05, Mann-Whitney U Test; Figure S1C).
103 To fully understand how S^3 would perform on more challenging primary cells, we next
104 applied it to human PBMCs (Figure S1C and S2), benchmarking against our original Seq-Well
105 protocol as well as a commercial technology (10X genomics, V2 chemistry; hereafter 10x v2). For
106 these comparisons, we down-sampled all resulting data to an average of 42,000 reads per cell to
107 account for differences in sequencing depth across technologies. Critically, Seq-Well S^3 resulted
108 in significant improvements in the complexity of our sequencing libraries compared to 10x v2 as
109 determined by the number of transcripts and genes detected at matched read depth (P < 0.05,
110 Mann-Whitney U Test & Linear Regression; Figure 1B-C). To confirm that these overall
111 improvements were not driven by changes in the relative frequencies of different cell types
112 captured by each technology, we also examined each subset independently (Figure S2A-B). For
113 each cell type detected, we observed significant increases in the numbers of transcripts captured
114 and genes detected using S^3 for each pairwise comparison between techniques (P < 0.05,
115 Mann-Whitney U Test; CD4+ T cells, Seq-Well V1: 1,044 ± 62.3 UMIs/cell; 10x v2: 7,671 ± 103.9 bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.
116 UMIs/cell; Seq-Well S^3: 13,390 ± 253.4 UMIs/cell; Mean ± Standard Error of the Median (SEM);
117 Figure S2). Both Seq-Well S^3 and 10x v2 displayed increased sensitivity for transcripts and
118 genes relative to Seq-Well v1, but Seq-Well S^3 showed the greatest efficiency (defined as genes
119 recovered at matched read depth) to detect genes for each cell type (Figure 1D-E; Figure S2).
120 We sought to further understand whether these improvements resulted in enhanced
121 detection of biologically relevant genes typically under-represented in high-throughput single-cell
122 sequencing libraries (Tabula Muris Consortium et al., 2018). Importantly, genes that were
123 differentially detected (i.e., higher in S^3) within each cell type include numerous transcription
124 factors, cytokines and cell-surface receptors (Figure 1D-E). For example, among CD4+ T cells,
125 we observe significantly increased detection of cytokines (e.g., TGFB1 and TNF), surface
126 receptors (e.g., TGFBR and CCR4) and transcription factors (e.g., STAT6, and IRF4) (P< 0.05,
127 Chi-Square Test, Figure 1H and S2).
128 Finally, we performed an additional comparison of enriched human CD4+ T cells profiled
129 using Seq-Well S^3 and 10X v2, as well as by Smart-Seq2, a commonly implemented microtiter
130 plate-based approach (Figure 1F-G) (Picelli et al., 2013). Integrated analysis of aggregate gene
131 detection revealed that Seq-Well S^3 detects more genes per cell than 10x v2 and nearly as many
132 genes per cell as Smart-Seq2 in pairwise comparison of techniques (10x v2: 2,057 ± 18.7
133 genes/cell , Seq-Well S^3: 3,514 ± 36.2 genes/cell , SS2: 3,975 ± 74.0 genes/cell; mean ± SEM;
134 P < 0.05, Mann-Whitney Test; Figure 1F). Further, comparing the frequency of gene detection
135 between methods revealed crucial differences for transcription factors, cytokines and
136 receptors/ligands. Surprisingly, we observe similar rates of gene detection between S^3 and
137 Smart-Seq2 for a large number of biologically informative genes (Figure S2F). Critically, while
138 comparable numbers of genes were detected across methods, Seq-Well S^3 detected more
139 genes per aligned read than either 10x v2 or SS2 in pairwise comparisons (P<0.05, Mann-
140 Whitney U Test; Figure 1G). bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.
141 A Resource of Cellular States Across Healthy and Inflamed Skin
142 To demonstrate the utility of Seq-Well S^3 to comprehensively describe cellular states
143 across human pathology at unprecedented resolution, we applied it to profile human skin samples
144 spanning multiple, complex inflammatory skin conditions (Figure 2) – including acne, alopecia
145 areata, granuloma annulare, leprosy, psoriasis – as well as normal skin (Figure 2A-B and S3A-
146 C). In total, we processed nine skin biopsies by S^3 and, after data quality filtering, retained
147 20,308 high-quality single-cell transcriptomes (Figure 2A-B).
148 To examine similarities and differences among these cells across the high-dimensional
149 gene expression space, we selected variable genes, performed UMAP dimensionality reduction,
150 and identified 33 clusters through Louvain clustering in Scanpy (Wolf et al., 2018) (Figure 2 and
151 S3A-C). To collapse clusters to cell-types, we performed enrichment analyses to identify cluster-
152 defining genes (Figure S3B) and then manually assigned cell-type identities based on the
153 expression of known lineage markers (Figure 2C). We also generated aggregate gene
154 expression profiles and performed hierarchical clustering using a combined list of the top 50
155 cluster-defining genes for each cluster to further support our annotations and groupings (Figure
156 S3C). Ultimately, we recovered a total of 16 primary cell-types, within which there was
157 considerable heterogeneity. The identified cell types include: B cells (marked by expression of
158 MS4A1 and CD79A), dendritic cells (FCER1G and CLEC10A), endothelial cells (SELE and
159 CD93), fibroblasts (DCN and COL6A2), hair follicles (SOX9), keratinocytes (KRT5 and KRT1),
160 macrophages (CD68 and CTSS), mast cells (CPA3 and IL1RL1), muscle (NEAT1 and
161 KCNQ1OT1), plasma cells (IGHG1), Schwann cells (SCN7A), and T cells (CD3D and TRBC2)
162 (Figure 2 and S3A-D). We next sought to define nuanced cell states within these immune, stromal
163 and parenchymal populations – including T cells, myeloid cells, endothelial cells, dermal
164 fibroblasts, and keratinocytes – across the spectrum of skin inflammation.
165 bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.
166 Seq-Well S^3 describes T cell states across inflammatory skin conditions
167 To determine the range of biological diversity that can be captured using Seq-Well S^3,
168 we first focused on further characterizing T cells across the inflammatory skin conditions
169 examined since each is known to significantly skew T cell phenotypes (Figure 3) (Diani et al.,
170 2015; Lowes et al., 2014). We performed dimensionality reduction and sub-clustering across T
171 cells alone (Figure 3A-B). Our analysis revealed nine sub-clusters that closely correspond to NK
172 cells and CD8+ T cells, as well as several known CD4+ T-helper cell (Th) subsets. As before, we
173 used the enhanced sensitivity of S^3 for lineage defining transcripts to help annotate the identity
174 of each sub-cluster; for example, in T cell sub-clusters 5 and 6, respectively, we detected distinct
175 expression of canonical regulatory T cell and Th-17 T cell transcription factors (e.g., FOXP3 and
176 RORC, respectively) and immune receptors (e.g. TIGIT and CXCR6 respectively) (Figure 3C-E
177 and S4). Additionally, we cross-referenced each sub-cluster’s marker genes against a series of
178 curated signatures in the Savant database (Lopez et al., 2017) to confirm our assignments. This
179 analysis highlighted similarity to previously characterized T cell and NK cell populations (Figure
180 S4C).
181 We next examined T cell phenotypes across inflammatory skin conditions to explore
182 variability in T cell subset composition by skin pathology (Figure 3B). This analysis revealed
183 potentially varied contributions to different classes of cutaneous inflammation. For example, sub-
184 cluster 6 is enriched for expression of canonical Th-17 genes including RORC, which encodes
185 the Th-17 lineage-defining transcription factor ROR�t (Ivanov et al., 2006) and is observed
186 predominantly within the leprosy sample. While either Th1 or Th2 responses are typically thought
187 to predominate in the immune response to leprosy, a role for Th-17 cells in controlling disease
188 has been previously demonstrated (Saini et al., 2013, 2016). We further found that sub-cluster 1,
189 which express NR4A1, a transcription factor that is a marker of dysfunctional T cells (Liu et al.,
190 2019), and sub-cluster 3, enriched for genes involved in nuclear organization (ANKRD36, XIST, bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.
191 and NEAT1), were over-represented in both patients from psoriasis (Figure 3B-C). In alopecia
192 areata, we detected a unique population of T cells (sub-cluster 7) that overexpress PDE4D, which
193 has been shown to plays a role in TCR-dependent T cell activation (Figure 3D) (Peter et al.,
194 2007).
195 We also uncovered considerable variation across cytotoxic T cells and NK cells. Directed
196 analysis within CD8+ T cells (sub-cluster 0) revealed a sub-grouping of activated CD8+ T cells that
197 express elevated levels of several inflammatory cytokines (TNF, CCL4, and XCL1), as well as
198 specific affinity receptors (FASLG and TNFRSF9) and transcription factors (KLF9 and EGR2);
199 this phenotypic skewing was observed primarily in a patient with granuloma annulare (Figure 3F
200 and S4B). Meanwhile, we found the highest degree of cytotoxic gene expression (GNLY, GZMB,
201 and PRF1) among cells in sub-cluster 8, suggesting that this sub-cluster may represent a diverse
202 set of NK cells, gd T cells, and activated cytotoxic T cells. Indeed, further analysis of sub-cluster
203 8 revealed 3 distinct component sub-groups of cytotoxic cells: a sub-group of CD8+ T cells (T.8.1;
204 TNFSF8, SLAMF1, CLEC2D, CD5) expressing various TCR genes; a second sub-group of CD16+
205 cells (T.8.2) expressing cytotoxic effector molecules (GNLY, PRF1, GZMB) and NK surface
206 receptors, consistent with either NK cell or tri-cytotoxic CTL (Balin et al., 2018); and a third sub-
207 group of NK cells (T.8.3) enriched for expression of c-KIT, RANKL (TNFSF11) and GITR
208 (TNFSFR18) (Figure 3H and S4B) (Söderström et al., 2010).
209 Profiling of T cell receptor expression is critical to understand T cell antigen specificity
210 (Zhang et al., 2018). Importantly, among CD4+ T cells obtained from peripheral blood, we
211 recovered most TCR-V and TCR-J genes at a higher frequency using Seq-Well S^3 as compared
212 to 10x v2 (P< 0.05, Chi-square Test; Figure S4C). Among CD4+ T cells from peripheral blood, we
213 observed paired detection of TRAC and TRBC in 1,293 of 1,485 CD4+ T cells (87.1% Paired
214 Detection Rate, Figure S4C). In the setting of skin inflammation, we explored TCR detection rates
215 across a range of sequencing read depths. Overall, we detected TRAC in 54.5%, TRBC in 75.5%, bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.
216 and paired detection in 46.4% of T cells (Figure 3G). Among T cells with at least 25,000 aligned
217 reads, we recovered paired a and b chains in 66.7%. Among cells from sub-cluster 8, we observe
218 expression of g and d constant genes (TRGC and TRDC), while remaining T cell clusters
219 exclusively express a and b TCR constant genes (Figure S4C). These data further suggest that
220 sub-cluster 8 represents a diverse population of gd, NK, and cytotoxic CD8+ T cells that share
221 common gene expression features and, potentially, roles in inflammation.
222
223 Spectrum of Myeloid Cell States in Skin Inflammation
224 In the setting of cutaneous inflammation, myeloid cells play a key role in maintaining tissue
225 homeostasis, wound healing and response to pathogens (Malissen et al., 2014). Using Seq-Well
226 S^3, we were able to identify numerous myeloid cell subpopulations defined by a combinations
227 of surface markers, cytokines and lineage-defining transcription factors. Specifically, we
228 independently analyzed 2,371 myeloid cells and identified nine sub-clusters representing 4
229 primary myeloid cell types based on expression of canonical lineage markers and comparison to
230 cell-type signatures in the Savant database: dendritic cells (CLEC10A), Langerhans cells (CD207
231 and CD1A), macrophages (CD68 and CD163), and mast cells (CPA3 and TPSAB1) (Figure 4A
232 and S4D-E) (Lopez et al., 2017).
233 Among the macrophages, our data reveal two distinct sub-clusters (Figure 4A-B). One
234 macrophage sub-cluster spans normal skin as well as multiple types of skin inflammation and is
235 characterized by elevated expression of previously characterized markers of dermal
236 macrophages (CD163, STAB1, and CEPP) (Fuentes-Duculan et al., 2010). The second sub-
237 cluster, meanwhile, is observed uniquely in leprosy and defined by genes involved in extracellular
238 proteolysis (LYZ, CHIT1, and CHI3L1) (Di Rosa et al., 2013). bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.
239 Skin functions as both a physical and immunologic barrier, and is the primary site of
240 exposure to environmental antigens. As such, multiple types of antigen-presenting cells (APCs)
241 are distributed in both the dermis and epidermis. In the epidermis, there is a specialized population
242 of antigen-presenting cells known as Langerhans cells. We initially identified Langerhans cells on
243 the basis of expression of canonical markers (CD207, CD1A; Figure 4C-D) (Romani et al., 2003).
244 For biopsies obtained from normal skin and leprosy, we performed MACS enrichments from the
245 epidermal section and loaded Langerhans cells as 5% of the total amount to increase recovery.
246 When we directly compared Langerhans cells from leprosy and normal skin, we observed
247 elevated expression of IDO1, STAT1, HCAR3 and MHC class I molecules (HLA-A, HLA-B and
248 HLA-F) in Langerhans cells in leprosy infection, which may suggest a role for Langerhans cells in
249 priming CD8+ T cell responses in this disease (Figure 4E) (Hunger et al., 2004; Pinheiro et al.,
250 2018).
251 Additionally, we found a large sub-group of dermal dendritic cells (Figure 4A). Further
252 analysis of the CD207- dendritic cell sub-cluster revealed multiple sub-groupings of dermal
253 dendritic cells across skin biopsies. Consistent with previous observations from peripheral blood
254 (Villani et al., 2017), we saw a sub-group of dendritic cells that corresponds to cDC1 (CLEC9A,
255 IRF8, and WDFY4) (P<0.05, Permutation Test, Figure S4H). We further report another sub-group
256 that represents cDC2 cells (IRF4, SOCS2, SLCO5A1, CD1B, CD1E) (Figure 4B-C and Figure
257 S4F-H) (Guilliams et al., 2016). Importantly, we detect expression of IL12B, a subunit of the IL-23
258 cytokine, within the sub-group of IRF4+ cDC2 cells (Figure S4I-J), which have previously been
259 shown to promote mucosal type 17 inflammation via secretion of IL-23 (Schlitzer et al., 2013).
260 Further, this sub-grouping of cDC2 cells express high levels of CCL17 and CCL22, chemokines
261 involved in T cell chemotaxis (Figure S4J) (Stutte et al., 2010).
262 We further identified three sub-groups of dermal dendritic cells that are broadly
263 distinguished from conventional dendritic cell clusters by expression of CLEC10A (Figure S4J), bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.
264 which has been shown to influence T cell cytokine responses in skin (Kashem et al., 2015;
265 Kumamoto et al., 2013). Cells from dermal DC sub-group 1 show elevated expression of CD44,
266 IL8 and SOD2 (Figure S4I). Cells from dermal DC sub-group 2 display elevated expression of
267 pro-inflammatory chemokines up-regulated during DC maturation (CXCL3, CCL2 and CCL4) (Jin
268 et al., 2010) and soluble mediators (EREG and INHBA). Finally, a third sub-grouping of dermal
269 DCs (Dermal DC3) was distinguished by expression of FCER1A, FCGR2A, and FCGR2B, which
270 are important for interfacing with humoral immunity (Figure S4I) (Guilliams et al., 2014).
271 In the skin, mast cells are most commonly associated with allergic responses, but mast
272 cell proteases serve additional roles in inflammation and pathogen defense (Pejler et al., 2010).
273 Among skin mast cells, we detect core expression of HDC (Histidine decarboxylase), HPGD, and
274 TPSAB1 (Tryptase a/b 1) (Figure 4F) (Dwyer et al., 2016). Importantly, we observe variable
275 expression of mast cell proteases TPSD1 (Tryptase D1) and CMA1 (Chymase A1), which are
276 primary mast cells effector molecules (Pejler et al., 2010), which may have functional
277 consequences. By performing analysis across inflammatory conditions and patients, we identify
278 a distinct pattern of mast cells with elevated expression of proteases (TPSD1, Tryptase D1 and
279 PRSS27, serine protease 27), SCG2 (secretogranin 2), and CCL2 in a patient with granuloma
280 annulare (Figure 4F).
281
282 Detection of Endothelial Heterogeneity and Vascular Addressin Expression
283 Multiple types of endothelial cells exist within the dermis of the skin. As in most tissues,
284 arterioles shuttle oxygenated blood to tissues terminating in a capillary bed that gives rise to post-
285 capillary venules. Importantly, DARC+ post-capillary venules are the primary site of egress of
286 immune cells from circulation into tissues (Schön et al., 2003). Using the improved sensitivity of
287 Seq-Well S^3, we sought to understand the spectrum of endothelial cell diversity and vascular
288 addressin expression across multiple instances of skin inflammation (von Andrian and Mempel, bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.
289 2003). We performed sub-clustering and dimensionality reduction across 4,996 endothelial cells
290 (Figure S5A-B) and identified three primary sub-clusters of dermal endothelial cells defined by
291 distinct expression patterns: vascular smooth muscle (TAGLN), endothelial cells (CD93) and
292 lymphatic endothelial cells (LYVE1) (Figure S5C). Importantly, we found multiple sub-clusters of
293 CD93+ endothelial across normal and inflamed skin biopsies (Figure S5A-B). For example, we
294 observe two distinct populations of endothelial cells: a population of DARC-, CD93+ endothelial
295 cells (Venule sub-cluster 3) that displays elevated expression of SLC9A3R2, which is involved in
296 endothelial homeostasis (Bhattacharya et al., 2012), and another cluster of proliferating
297 endothelial cells (Venule sub-cluster 4) (Figure S5D).
298 Further, we sought to understand the distribution of vascular addressins expressed by
299 DARC+ endothelial sub-populations, the site primary site of lymphocyte egress into tissues
300 (Figure S5E) (Thiriot et al., 2017). Notably, across sub-populations of CD93+ endothelial cells
301 (Venule sub-clusters 1-4), we observe variation in expression of vascular addressins (Figure
302 S5E). Among post-capillary venules, we observe broadly elevated expression of ITGA5, ITGA6,
303 ITGB4, ICAM2, and ITGA2, while arterioles express higher levels of ITGA7, ITGA8, and ITGB5.
304 Further, we observe the highest expression of ITGA4, ITGA9, ITGB2 and ITGB8 among lymphatic
305 endothelial cells (Figure 5E).
306
307 Altered Dermal Fibroblast Identities in Skin Inflammation
308 Dermal fibroblasts provide structural support and are the primary source of extracellular
309 matrix components within the skin. Previous studies have demonstrated significant variation
310 among dermal fibroblasts based on their relationship to anatomic features of the skin (Driskell
311 and Watt, 2015; Driskell et al., 2013). To deeply catalogue diverse fibroblast cell states across
312 inflamed skin, we performed dimensionality reduction and sub-clustering within the 4,189
313 fibroblasts identified across all samples and conditions (Figure S5F-G). In comparison to inflamed bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.
314 biopsies, fibroblasts from normal skin display enrichments in LTBP4, IGFBP5, and TCF4.
315 Consistent with previous single-cell studies of dermal fibroblasts, we observe a sub-population of
316 fibroblasts (Cluster 6) that express COL11A1, DPEP1 and RBP4, where these cells were
317 suggested to have a role in connective tissue differentiation (Figure S5H) (Tabib et al., 2018).
318 Fibroblasts from GA patient 1 (sub-cluster 2) express elevated levels of SPOCK1, CRLF1,
319 and COMP, a cartilage protein that is upregulated in matrix-producing fibroblasts following
320 myocardial infarction (Fu et al., 2018) (Figure S5H-I). Further, fibroblasts from GA patient 2 (sub-
321 cluster 0) display elevated expression of protease inhibitor 16 (PI16), which inhibits the function
322 of MMP2 (Hazell et al., 2016), and ITIH5, a protease inhibitor important for maintenance of dermal
323 hyaluronic acid that is overexpressed in skin inflammation (Figure S5H-I) (Huth et al., 2015).
324 Finally, among fibroblasts from acne patients, we observed elevated expression of multiple
325 metallothioneins (Figure S5H-I). Specifically, the expression levels of MT1E and MT2A are
326 highest in fibroblasts and endothelial cells in acne (Figure S5H). As seen among endothelial cells,
327 fibroblast expression patterns in acne are consistent with a wound healing response (Iwata et al.,
328 1999).
329 Keratinocyte Differentiation Trajectories
330 Within the epidermis, keratinocytes undergo a stereotyped differentiation process in which
331 cells acquire altered morphology and phenotype as they mature (Figure 5A) (Fuchs, 1990).
332 Under physiologic conditions, basal keratinocytes are characterized by expression of KRT14 and
333 TP63, and continuously divide to give rise to the remaining cells of the epidermis (Fuchs and
334 Raghavan, 2002). Using keratinocytes from normal skin, we performed pseudo-temporal analysis
335 to reconstruct the differentiation process of normal epidermal keratinocytes (Figure 5D). More
336 specifically, in normal skin, we first identified a population of keratinocytes enriched for expression
337 of TP63 and KRT14, markers of basal keratinocytes (Figure S6B) (Pellegrini et al., 2001). We
338 then used known patterns of cytokeratin expression to infer localization of keratinocytes along a bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.
339 supervised differentiation trajectory (Figure 5E and S6A) (Ordovas-Montanes et al., 2018). Our
340 trajectory analysis revealed patterns of transcription factor and cytokeratin expression that closely
341 correspond to previously established signatures of keratinocyte maturation (Cheng et al., 2018).
342 Consistent with immunohistochemical staining from the Human Protein Atlas (Figure 5E) (Uhlén
343 et al., 2015), we observed enrichment of filaggrin (FLG), a protein in the outer layers of the
344 epidermis (Sandilands et al., 2009), mRNA among keratinocytes that lie at the terminal points in
345 the pseudo-temporal ordering (Figure 5E and Figure S6B).
346 Having established a trajectory for normal keratinocyte differentiation, we next examined
347 patterns of keratinocyte differentiation across pathologic conditions. To identify conserved and
348 unique patterns across conditions, we constructed a combined diffusion map using the 5,141
349 keratinocytes recovered across all samples (Figure 5C). While keratinocytes from most
350 conditions closely align with normal differentiation, we observe marked deviation in the
351 differentiation trajectory of psoriatic keratinocytes (Figure 5C). Consistent with previous
352 observations, differential expression analysis reveals significant up-regulation of antimicrobial
353 peptides (S100A7, S100A8, S100A9) and pro-inflammatory cytokines (IL36G, IL36RN) in
354 psoriatic keratinocytes (Li et al., 2014).
355 Based on increased sensitivity of Seq-Well S^3 to detect transcription factors observed in
356 peripheral lymphocytes, we hypothesized that our data might enable identification of novel
357 transcriptional regulators of psoriatic keratinocytes. To identify potential drivers of the psoriatic
358 disease process within the epidermis, we performed differential pseudo-time correlation analysis
359 between psoriatic and normal keratinocytes. Specifically, we separately constructed pseudo-time
360 trajectories for normal and psoriatic keratinocytes, calculated correlation values between diffusion
361 pseudo-time and gene expression levels, and examined the difference in correlation values
362 between psoriatic and normal keratinocytes (Figure 5F and S6A-B). Notably, we observed
363 positive correlation of FOSL1, an AP-1 transcription factor, with diffusion pseudo-time in psoriatic bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.
364 keratinocytes, implying that FOSL1 is preferentially expressed along the differentiation trajectory
365 of psoriatic keratinocytes. To validate this observation, we performed immunofluorescence
366 staining for FOSL1 protein, and measured increased levels of FOSL1 in psoriatic skin (Figure
367 5G). We further validated the distribution of additional genes overexpressed or differentially
368 correlated with diffusion pseudo-time in psoriatic keratinocytes (including TNFAIP3, IL36G, and
369 APOBEC3) at the protein level (Figure 5G and S6A).
370 To further define differences in gene expression patterns between normal and psoriatic
371 keratinocytes, we scored the expression levels of known cytokine response signatures using a
372 series of reference signatures gene lists derived from population RNA-Seq of cultured
373 keratinocytes exposed to IL-17A (Figure S6C). While IL-17 has been previously implicated in the
374 pathogenesis of psoriasis, here we infer the identity of cells that dominate the IL-17 response,
375 localizing the expression of IL-17 responsive genes to spinous keratinocytes (Ordovas-Montanes
376 et al., 2018). To validate this observation, we performed immunofluorescent staining for IL-17R
377 protein and measured the highest staining within spinous keratinocytes exclusively within psoriatic
378 skin (Figure 5H). Collectively, these data provide novel insights into the localization IL-17
379 response in psoriatic keratinocytes.
380
381 DISCUSSION
382 Here, we present an enhanced technique for high-throughput scRNA-Seq – Seq-Well S^3
383 – that affords improved sensitivity for transcript capture and gene detection. Through use of a
384 templated second-strand synthesis, S^3 recovers information typically lost in bead-based high-
385 throughput scRNA-Seq protocol such as Seq-Well or Drop-Seq. Specifically, S^3 reclaims mRNA
386 molecules that are successfully captured and reverse transcribed but not labeled with a second
387 primer sequence through template switching (Figure 1 and S1). Using Seq-Well S^3, we obtain
388 a 5-10 fold increase in the number of unique molecules captured from cells at similar sequencing bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.
389 depth relative Seq-Well v1 (Figures 1, S1 and S2) (Gierahn et al., 2017). Beyond aggregate
390 increases in the number of transcripts recovered per-cell, the improvements in sensitivity made
391 possible by Seq-Well S^3 enable enhanced detection, and thus deeper examination, of lineage-
392 defining factors in immune and parenchymal cells – such as transcription factors, cytokines, and
393 cytokine receptors among lymphocytes (Figure 1 and S2) – which are often transiently or lowly
394 expressed (Zhu et al., 2010). Among CD4+ T cells isolated from PBMCs, for example, we
395 observed rates of gene detection similar to those observed in Smart-Seq2, a best-in-class
396 microtiter plate-based method (Figure 1F-G and S2F).
397 Similarly, using Seq-Well S^3, we report improved paired detection of a and b TCR
398 sequences from T cells in peripheral blood and tissue biopsies (Figures 3G and S4C). Among
399 CD4+ T cells from PBMCs, we recover paired TCR a and b constant genes in 87.1% of cells.
400 Together with targeted enrichment, amplification and sequencing, we anticipate that Seq-Well
401 S^3 will enable improvements in TCR reconstruction and deep characterizations of clonotype-
402 phenotype relationships at scale (Zhang et al., 2018). Collectively, our validation experiments
403 show that Seq-Well S^3 significantly augments the amount of information that can be recovered
404 in massively-parallel scRNA-seq experiments, enabling high-resolution profiling of low-input
405 biopsy samples at scale.
406 With this enhanced method, here, we move towards a draft atlas of human skin
407 inflammation by creating a compendium of cell-types and states for the broader research
408 community (Regev et al., 2018). Through use of Seq-Well S^3, we survey, at unprecedented
409 resolution, the diversity of cell-types and states – e.g., among tissue resident T cells and myeloid
410 cells – present across multiple types of skin inflammation. For example, GA and leprosy are two
411 granulomatous diseases characterized by aggregates of lymphocytes and macrophages within
412 the dermis, which are both thought to arise from a delayed-type hypersensitivity response to M.
413 leprae infection (leprosy) and an unknown agent (GA) (Modlin et al., 1984; Terziroli Beretta-Piccoli bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.
414 et al., 2018). Here, we find that both are characterized by the presence of T cell sub-cluster 0
415 (Immature CD8+ CTL) and T cell sub-cluster 8 (mature CTL effectors containing CD8+ T-CTL, gd
416 and NK cells; Figure 3). Although both conditions contain CD163+ dermal macrophages and
417 various DC subpopulations, M1-like macrophages were present only in leprosy, which host the
418 intracellular pathogen M. leprae, were present only in leprosy (Fulco et al., 2014; Verreck et al.,
419 2004). Moreover, GA uniquely contained specific populations of fibroblasts expressing SPOCK1,
420 CRLF1, and COMP (Figure S5), which likely reflect remodeling of the dermis with mucin
421 deposition and alternation of elastin fibers (Piette and Rosenbach, 2016; Yun et al., 2009).
422 Acne, meanwhile, is an inflammatory disease thought to arise in response to infection with
423 P. acnes, resulting in the formation of lesions that resemble a wound following eruption of the hair
424 follicle into the dermis (Beylot et al., 2014). Here, we observe 2 clusters of endothelial cells marked
425 by expression of SLC9A3R2, a marker of endothelial homeostasis, and a signature of proliferation
426 (Venule clusters 3 and 4, Figure S5). This increased angiogenesis and endothelial proliferation
427 is most consistent with the proliferative phase of wound healing in acne (Holland et al., 2004).
428 Alopecia areata and psoriasis both arise from autoimmune and autoinflammatory
429 processes, yet there were distinct differences in their underlying cell states. For example, alopecia
430 areata is thought to be driven by a population of CD8+ T cells that target hair follicles (Xing et al.,
431 2014). Notably, in alopecia, we report a sub-cluster of T cells characterized by expression of
432 PDE4D (Figure 3). PDE4 inhibitors have recently shown demonstrated efficacy in the treatment
433 of alopecia (Keren et al., 2015; López et al., 2017), and it is intriguing to speculate that these
434 inhibitors might work by targeting this subset of T cells.
435 In psoriasis, T cells are thought to be a primary driver of inflammation, with dendritic cells
436 playing a central role in the recruitment and polarization of T cells that contribute to the
437 hyperproliferation of keratinocytes in the disease (Lowes et al., 2014). In both patients with
438 psoriasis, we report a sub-cluster of DCs (IRF4+ cDC2) that display elevated expression of bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.
439 CCL17, CCL22 and IL12B (Figure 4G and Figure S4I). Importantly, a similar population of dermal
440 cDC2 cells has recently been shown to drive psoriatic inflammation in mice and humans through
441 the recruitment of inflammatory T cells (Kim et al., 2018; Zaba et al., 2010). Although we detected
442 a diversity in T cell subtypes in psoriatic lesions, we note few Th-17-like cells (Hawkes et al.,
443 2018).
444 Leveraging the increased sensitivity of Seq-Well S^3, we performed pseudo-time
445 correlation analysis to uncover an altered differentiation trajectory of keratinocytes compared to
446 normal skin (Figure 5 and S6). From our pseudo-time correlation analysis, we detected FOSL1
447 as a putative transcription factor involved in psoriatic differentiation, a finding which we validated
448 through immunofluorescent staining of healthy and psoriatic skin (Figure 5G). Further, previous
449 studies using in vitro keratinocyte based systems have suggested that more differentiated
450 keratinocytes were the main responders to IL-17A, given larger effect sizes in differentiated
451 compared to monolayer keratinocyte (Chiricozzi et al., 2014). Using data generated with Seq-Well
452 S^3 cross-analyzed against an IL-17 response signature in keratinocytes, we show that IL-17
453 responses are observed in keratinocytes from all layers of the epidermis, but that these responses
454 are stronger in keratinocytes derived from more differentiated layers of the psoriatic epidermis
455 (Figure S6C). This observation is corroborated by co-localization of the IL-17 receptor subunits
456 (IL-17RA/IL-17RC) in the upper layers of psoriatic epidermis (Figure 5H).
457 In conclusion, we describe a powerful massively-parallel scRNA-Seq protocol that enables
458 improved transcript capture and gene detection from low-input clinical samples. Here, Seq-Well
459 S^3 provides novel insights into putative mechanisms and the cellular localization of previously
460 appreciated and unknown responses to specific inflammatory mediators in immunologic skin
461 conditions in a fashion not previously achievable. Increases in the sensitivity of gene and
462 transcript detection are increasingly important as single-cell atlasing efforts shift from detection of
463 large differences between cell types within normal tissue to identification of subtle differences in bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.
464 cell state across cell types within diseased tissues. The increased sensitivity of gene detection
465 and transcript capture afforded by S^3 enhances the strength of inferences that can be drawn
466 from these types of single-cell data, as evidenced by the range of immune, stromal and
467 parenchymal cell states uncovered in human skin inflammation. The S^3 protocol is easy to
468 integrate into current bead-based RNA-Seq platforms, such as Drop-Seq, making it broadly useful
469 for the single-cell community, particularly in the setting of human disease. Importantly, S^3’s
470 increases in library complexity and sequencing efficiency reduce costs relative to plate-based
471 protocols, and providing researchers with a powerful and cost-effective alternative to commercial
472 solutions in a format that can be deployed almost anywhere. bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.
473 ACKNOWLEDGEMENTS
474 This work was supported in part by the Koch Institute Support (core) NIH Grant P30-CA14051 475 from the National Cancer Institute, as well as the Bridge Project, a partnership between the Koch 476 Institute for Integrative Cancer Research at MIT and the Dana-Farber/Harvard Cancer Center. 477 This work was also supported by the Food Allergy Science Initiative at the Broad Institute and the 478 NIH (5P01AI039671, 5U19AI089992). A.K.S. was supported by the Searle Scholars Program, the 479 Beckman Young Investigator Program, the Pew-Stewart Scholars Program for Cancer Research, 480 a Sloan Fellowship in Chemistry, the NIH (1DP2GM119419, 2U19AI089992, 2R01HL095791, 481 1U54CA217377, 2P01AI039671, 5U24AI118672, 2RM1HG006193, 1R33CA202820, 482 1R01AI138546, 1R01HL126554, 1R01DA046277, 1U2CCA23319501) and the Bill and Melinda 483 Gates Foundation (OPP1139972, OPP1202327, OPP1137006, and OPP1202327). R.L.M. was 484 supported by the NIH (R01 AI022553, AR040312, AR074302). J.G. is supported by the Taubman 485 Medical Research Institute and the NIH (R01-AR060802, R01-AI30025, and P30-AR075043). 486 T.K.H. is supported by the NIH F30-AI143160.
487
488 CONTRIBUTIONS
489 T.K.H., M.W.H., T.M.G., R.L.M., J.C.L., and A.K.S. designed the study. T.K.H., M.H.W., T.D., 490 D.W., P.A., and B.A. collected skin samples and performed single-cell sequencing experiments. 491 S.S., L.C.T., and J.E.G. performed immunofluorescent staining. T.K.H., M.H.W., T.M.G., and 492 F.M. analyzed data under the guidance of J.O.M., R.L.M., J.C.L., and A.K.S. T.K.H., M.H.W., 493 T.M.G., J.C.L., and A.K.S. wrote the manuscript with input from all authors.
494
495 DECLARATION OF INTERESTS
496 A.K.S, and J.C.L. have received compensation for consulting and SAB membership from 497 Honeycomb Biotechnologies. A.K.S. has received compensation for consulting and SAB 498 membership from Cellarity, Cogen Therapeutics, and Dahlia Biosciences. T.M.G., T.K.H., 499 M.H.W., A.K.S., and J.C.L are co-inventors on a provisional patent application filed by MIT relating 500 to the improved methodology described in this manuscript.
501 bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.
502 REFERENCES
503 Von Andrian, U.H., and Mempel, T.R. (2003). Homing and cellular traffic in lymph nodes. Nat. 504 Rev. Immunol. 3, 867.
505 Balin, S.J., Pellegrini, M., Klechevsky, E., Won, S.T., Weiss, D.I., Choi, A.W., Hakimian, J., Lu, 506 J., Ochoa, M.T., Bloom, B.R., et al. (2018). Human antimicrobial cytotoxic T lymphocytes, 507 defined by NK receptors and antimicrobial proteins, kill intracellular bacteria. Sci. Immunol. 3, 508 eaat7668.
509 Beylot, C., Auffret, N., Poli, F., Claudel, J.-P., Leccia, M.-T., Giudice, P.D., and Dreno, B. 510 (2014). Propionibacterium acnes: an update on its role in the pathogenesis of acne. J. Eur. 511 Acad. Dermatol. Venereol. 28, 271–278.
512 Bhattacharya, R., Wang, E., Dutta, S.K., Vohra, P.K., E, G., Prakash, Y.S., and Mukhopadhyay, 513 D. (2012). NHERF-2 maintains endothelial homeostasis. Blood 119, 4798–4806.
514 Braga, F.A.V., Kar, G., Berg, M., Carpaij, O.A., Polanski, K., Simon, L.M., Brouwer, S., Gomes, 515 T., Hesse, L., Jiang, J., et al. (2019). A cellular census of human lungs identifies novel cell 516 states in health and in asthma. Nat. Med. 1.
517 Cheng, J.B., Sedgewick, A.J., Finnegan, A.I., Harirchian, P., Lee, J., Kwon, S., Fassett, M.S., 518 Golovato, J., Gray, M., Ghadially, R., et al. (2018). Transcriptional Programming of Normal and 519 Inflamed Human Epidermis at Single-Cell Resolution. Cell Rep. 25, 871–883.
520 Chiricozzi, A., Nograles, K.E., Johnson-Huang, L.M., Fuentes-Duculan, J., Cardinale, I., 521 Bonifacio, K.M., Gulati, N., Mitsui, H., Guttman-Yassky, E., Suárez-Fariñas, M., et al. (2014). IL- 522 17 Induces an Expanded Range of Downstream Genes in Reconstituted Human Epidermis 523 Model. PLOS ONE 9, e90284.
524 Di Rosa, M., Malaguarnera, G., De Gregorio, C., Drago, F., and Malaguarnera, L. (2013). 525 Evaluation of CHI3L-1 and CHIT-1 Expression in Differentiated and Polarized Macrophages. 526 Inflammation 36, 482–492.
527 Diani, M., Altomare, G., and Reali, E. (2015). T cell responses in psoriasis and psoriatic arthritis. 528 Autoimmun. Rev. 14, 286–292.
529 Driskell, R.R., and Watt, F.M. (2015). Understanding fibroblast heterogeneity in the skin. Trends 530 Cell Biol. 25, 92–99.
531 Driskell, R.R., Lichtenberger, B.M., Hoste, E., Kretzschmar, K., Simons, B.D., Charalambous, 532 M., Ferron, S.R., Herault, Y., Pavlovic, G., Ferguson-Smith, A.C., et al. (2013). Distinct fibroblast 533 lineages determine dermal architecture in skin development and repair. Nature 504, 277–281.
534 Dwyer, D.F., Barrett, N.A., Austen, K.F., The Immunological Genome Project Consortium, 535 Dwyer, D.F., Barrett, N.A., Austen, K.F., Kim, E.Y., Brenner, M.B., Shaw, L., et al. (2016). 536 Expression profiling of constitutive mast cells reveals a unique identity within the immune 537 system. Nat. Immunol. 17, 878–887.
538 Fuchs, E. (1990). Epidermal differentiation: the bare essentials. J. Cell Biol. 111, 2807–2814. bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.
539 Fuchs, E., and Raghavan, S. (2002). Getting under the skin of epidermal morphogenesis. Nat. 540 Rev. Genet. 3, 199.
541 Fuentes-Duculan, J., Suárez-Fariñas, M., Zaba, L.C., Nograles, K.E., Pierson, K.C., Mitsui, H., 542 Pensabene, C.A., Kzhyshkowska, J., Krueger, J.G., and Lowes, M.A. (2010). A subpopulation 543 of CD163-positive macrophages is classically activated in psoriasis. J. Invest. Dermatol. 130, 544 2412–2422.
545 Fulco, T. de O., Andrade, P.R., Barbosa, M.G. de M., Pinto, T.G.T., Ferreira, P.F., Ferreira, H., 546 Nery, J.A. da C., Real, S.C., Borges, V.M., Moraes, M.O., et al. (2014). Effect of Apoptotic Cell 547 Recognition on Macrophage Polarization and Mycobacterial Persistence. Infect. Immun. 82, 548 3968–3978.
549 Gierahn, T.M., Wadsworth Ii, M.H., Hughes, T.K., Bryson, B.D., Butler, A., Satija, R., Fortune, 550 S., Love, J.C., and Shalek, A.K. (2017). Seq-Well: portable, low-cost RNA sequencing of single 551 cells at high throughput. Nat. Methods 14, 395–398.
552 Guilliams, M., Bruhns, P., Saeys, Y., Hammad, H., and Lambrecht, B.N. (2014). The function of 553 Fcγ receptors in dendritic cells and macrophages. Nat. Rev. Immunol. 14, 94–108.
554 Guilliams, M., Dutertre, C.-A., Scott, C.L., McGovern, N., Sichien, D., Chakarov, S., 555 Van Gassen, S., Chen, J., Poidinger, M., De Prijck, S., et al. (2016). Unsupervised High- 556 Dimensional Analysis Aligns Dendritic Cells across Tissues and Species. Immunity 45, 669– 557 684.
558 Haghverdi, L., Buettner, F., and Theis, F.J. (2015). Diffusion maps for high-dimensional single- 559 cell analysis of differentiation data. Bioinformatics 31, 2989–2998.
560 Hawkes, J.E., Yan, B.Y., Chan, T.C., and Krueger, J.G. (2018). Discovery of the IL-23/IL-17 561 Signaling Pathway and the Treatment of Psoriasis. J. Immunol. 201, 1605–1613.
562 Hazell, G.G.J., Peachey, A.M.G., Teasdale, J.E., Sala-Newby, G.B., Angelini, G.D., Newby, 563 A.C., and White, S.J. (2016). PI16 is a shear stress and inflammation-regulated inhibitor of 564 MMP2. Sci. Rep. 6, 39553.
565 Hunger, R.E., Sieling, P.A., Ochoa, M.T., Sugaya, M., Burdick, A.E., Rea, T.H., Brennan, P.J., 566 Belisle, J.T., Blauvelt, A., Porcelli, S.A., et al. (2004). Langerhans cells utilize CD1a and langerin 567 to efficiently present nonpeptide antigens to T cells. J. Clin. Invest. 113, 701–708.
568 Huth, S., Heise, R., Vetter-Kauczok, C.S., Skazik, C., Marquardt, Y., Czaja, K., Knüchel, R., 569 Merk, H.F., Dahl, E., and Baron, J.M. (2015). Inter-α-trypsin inhibitor heavy chain 5 (ITIH5) is 570 overexpressed in inflammatory skin diseases and affects epidermal morphology in constitutive 571 knockout mice and murine 3D skin models. Exp. Dermatol. 24, 663–668.
572 Islam, S., Kjällquist, U., Moliner, A., Zajac, P., Fan, J.-B., Lönnerberg, P., and Linnarsson, S. 573 (2012). Highly multiplexed and strand-specific single-cell RNA 5’ end sequencing. Nat. Protoc. 574 7, 813–828.
575 Ivanov, I.I., McKenzie, B.S., Zhou, L., Tadokoro, C.E., Lepelley, A., Lafaille, J.J., Cua, D.J., and 576 Littman, D.R. (2006). The Orphan Nuclear Receptor RORγt Directs the Differentiation Program 577 of Proinflammatory IL-17+ T Helper Cells. Cell 126, 1121–1133. bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.
578 Kabashima, K., Honda, T., Ginhoux, F., and Egawa, G. (2019). The immunological anatomy of 579 the skin. Nat. Rev. Immunol. 19, 19–30.
580 Kapteyn, J., He, R., McDowell, E.T., and Gang, D.R. (2010). Incorporation of non-natural 581 nucleotides into template-switching oligonucleotides reduces background and improves cDNA 582 synthesis from very small RNA samples. BMC Genomics 11, 413.
583 Kashem, S.W., Riedl, M.S., Yao, C., Honda, C.N., Vulchanova, L., and Kaplan, D.H. (2015). 584 Nociceptive Sensory Fibers Drive Interleukin-23 Production from CD301b+ Dermal Dendritic 585 Cells and Drive Protective Cutaneous Immunity. Immunity 43, 515–526.
586 Kim, T.-G., Kim, S.H., Park, J., Choi, W., Sohn, M., Na, H.Y., Lee, M., Lee, J.W., Kim, S.M., 587 Kim, D.-Y., et al. (2018). Skin-Specific CD301b+ Dermal Dendritic Cells Drive IL-17−Mediated 588 Psoriasis-Like Immune Response in Mice. J. Invest. Dermatol. 138, 844–853.
589 Klein, A.M., Mazutis, L., Akartuna, I., Tallapragada, N., Veres, A., Li, V., Peshkin, L., Weitz, 590 D.A., and Kirschner, M.W. (2015). Droplet Barcoding for Single-Cell Transcriptomics Applied to 591 Embryonic Stem Cells. Cell 161, 1187–1201.
592 Kumamoto, Y., Linehan, M., Weinstein, J.S., Laidlaw, B.J., Craft, J.E., and Iwasaki, A. (2013). 593 CD301b+ Dermal Dendritic Cells Drive T Helper 2 Cell-Mediated Immunity. Immunity 39, 733– 594 743.
595 Li, B., Tsoi, L.C., Swindell, W.R., Gudjonsson, J.E., Tejasvi, T., Johnston, A., Ding, J., Stuart, 596 P.E., Xing, X., Kochkodan, J.J., et al. (2014). Transcriptome Analysis of Psoriasis in a Large 597 Case–Control Sample: RNA-Seq Provides Insights into Disease Mechanisms. J. Invest. 598 Dermatol. 134, 1828–1838.
599 Liu, X., Wang, Y., Lu, H., Li, J., Yan, X., Xiao, M., Hao, J., Alekseev, A., Khong, H., Chen, T., et 600 al. (2019). Genome-wide analysis identifies NR4A1 as a key mediator of T cell dysfunction. 601 Nature 567, 525.
602 Lopez, D., Montoya, D., Ambrose, M., Lam, L., Briscoe, L., Adams, C., Modlin, R.L., and 603 Pellegrini, M. (2017). SaVanT: a web-based tool for the sample-level visualization of molecular 604 signatures in gene expression profiles. BMC Genomics 18.
605 Lowes, M.A., Suárez-Fariñas, M., and Krueger, J.G. (2014). Immunology of psoriasis. Annu. 606 Rev. Immunol. 32, 227–255.
607 Lun, A.T.L., Riesenfeld, S., Andrews, T., Dao, T.P., Gomes, T., Marioni, J.C., and participants in 608 the 1st Human Cell Atlas Jamboree (2019). EmptyDrops: distinguishing cells from empty 609 droplets in droplet-based single-cell RNA sequencing data. Genome Biol. 20, 63.
610 Macosko, E.Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, 611 A.R., Kamitaki, N., Martersteck, E.M., et al. (2015). Highly Parallel Genome-wide Expression 612 Profiling of Individual Cells Using Nanoliter Droplets. Cell 161, 1202–1214.
613 Malissen, B., Tamoutounour, S., and Henri, S. (2014). The origins and functions of dendritic 614 cells and macrophages in the skin. Nat. Rev. Immunol. 14, 417–428. bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.
615 McGinnis, C.S., Murrow, L.M., and Gartner, Z.J. (2018). DoubletFinder: Doublet detection in 616 single-cell RNA sequencing data using artificial nearest neighbors. BioRxiv 352484.
617 Modlin, R.L., Horwitz, D.A., Jordan, R.R., Gebhard, J.F., Taylor, C.R., and Rea, T.H. (1984). 618 Immunopathologic Demonstration of T Lymphocyte Subpopulations and Interleukin 2 in 619 Graneloma Annulare. Pediatr. Dermatol. 2, 26–32.
620 Montoro, D.T., Haber, A.L., Biton, M., Vinarsky, V., Lin, B., Birket, S.E., Yuan, F., Chen, S., 621 Leung, H.M., Villoria, J., et al. (2018). A revised airway epithelial hierarchy includes CFTR- 622 expressing ionocytes. Nature 560, 319.
623 Ordovas-Montanes, J., Dwyer, D.F., Nyquist, S.K., Buchheit, K.M., Vukovic, M., Deb, C., 624 Wadsworth, M.H., Hughes, T.K., Kazer, S.W., Yoshimoto, E., et al. (2018). Allergic inflammatory 625 memory in human respiratory epithelial progenitor cells. Nature 560, 649.
626 Pejler, G., Rönnberg, E., Waern, I., and Wernersson, S. (2010). Mast cell proteases: 627 multifaceted regulators of inflammatory disease. Blood 115, 4981–4990.
628 Pellegrini, G., Dellambra, E., Golisano, O., Martinelli, E., Fantozzi, I., Bondanza, S., Ponzin, D., 629 McKeon, F., and Luca, M.D. (2001). p63 identifies keratinocyte stem cells. Proc. Natl. Acad. Sci. 630 98, 3156–3161.
631 Picelli, S., Björklund, Å.K., Faridani, O.R., Sagasser, S., Winberg, G., and Sandberg, R. (2013). 632 Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat. Methods 10, 633 1096–1098.
634 Picelli, S., Faridani, O.R., Björklund, Å.K., Winberg, G., Sagasser, S., and Sandberg, R. (2014). 635 Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171–181.
636 Piette, E.W., and Rosenbach, M. (2016). Granuloma annulare: Clinical and histologic variants, 637 epidemiology, and genetics. J. Am. Acad. Dermatol. 75, 457–465.
638 Pinheiro, R.O., Schmitz, V., Silva, B.J. de A., Dias, A.A., de Souza, B.J., de Mattos Barbosa, 639 M.G., de Almeida Esquenazi, D., Pessolani, M.C.V., and Sarno, E.N. (2018). Innate Immune 640 Responses in Leprosy. Front. Immunol. 9.
641 Prakadan, S.M., Shalek, A.K., and Weitz, D.A. (2017). Scaling by shrinking: empowering single- 642 cell “omics” with microfluidic devices. Nat. Rev. Genet. 18, 345–361.
643 Puel, A., Ziegler, S.F., Buckley, R.H., and Leonard, W.J. (1998). Defective IL7R expression in T 644 - B + NK + severe combined immunodeficiency. Nat. Genet. 20, 394.
645 Regev, A., Teichmann, S., Rozenblatt-Rosen, O., Stubbington, M., Ardlie, K., Amit, I., Arlotta, 646 P., Bader, G., Benoist, C., Biton, M., et al. (2018). The Human Cell Atlas White Paper. 647 ArXiv181005192 Q-Bio.
648 Romani, N., Holzmann, S., Tripp, C.H., Koch, F., and Stoitzner, P. (2003). Langerhans cells – 649 dendritic cells of the epidermis. APMIS 111, 725–740.
650 Sandilands, A., Sutherland, C., Irvine, A.D., and McLean, W.H.I. (2009). Filaggrin in the 651 frontline: role in skin barrier function and disease. J. Cell Sci. 122, 1285–1294. bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.
652 Sasagawa, Y., Nikaido, I., Hayashi, T., Danno, H., Uno, K.D., Imai, T., and Ueda, H.R. (2013). 653 Quartz-Seq: a highly reproducible and sensitive single-cell RNA sequencing method, reveals 654 non-genetic gene-expression heterogeneity. Genome Biol. 14, 3097.
655 Shishkin, A.A., Giannoukos, G., Kucukural, A., Ciulla, D., Busby, M., Surka, C., Chen, J., 656 Bhattacharyya, R.P., Rudy, R.F., Patel, M.M., et al. (2015). Simultaneous generation of many 657 RNA-seq libraries in a single reaction. Nat. Methods 12, 323–325.
658 Söderström, K., Stein, E., Colmenero, P., Purath, U., Müller-Ladner, U., Matos, C.T. de, Tarner, 659 I.H., Robinson, W.H., and Engleman, E.G. (2010). Natural killer cells trigger osteoclastogenesis 660 and bone destruction in arthritis. Proc. Natl. Acad. Sci. 107, 13028–13033.
661 Tabib, T., Morse, C., Wang, T., Chen, W., and Lafyatis, R. (2018). SFRP2/DPP4 and 662 FMO1/LSP1 Define Major Fibroblast Populations in Human Skin. J. Invest. Dermatol. 138, 802– 663 810.
664 Tabula Muris Consortium, Overall coordination, Logistical coordination, Organ collection and 665 processing, Library preparation and sequencing, Computational data analysis, Cell type 666 annotation, Writing group, Supplemental text writing group, and Principal investigators (2018). 667 Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris. Nature 562, 367–372.
668 Terziroli Beretta-Piccoli, B., Mainetti, C., Peeters, M.-A., and Laffitte, E. (2018). Cutaneous 669 Granulomatosis: a Comprehensive Review. Clin. Rev. Allergy Immunol. 54, 131–146.
670 Thiriot, A., Perdomo, C., Cheng, G., Novitzky-Basso, I., McArdle, S., Kishimoto, J.K., Barreiro, 671 O., Mazo, I., Triboulet, R., Ley, K., et al. (2017). Differential DARC/ACKR1 expression 672 distinguishes venular from non-venular endothelial cells in murine tissues. BMC Biol. 15, 45.
673 Uhlén, M., Fagerberg, L., Hallström, B.M., Lindskog, C., Oksvold, P., Mardinoglu, A., Sivertsson, 674 Å., Kampf, C., Sjöstedt, E., Asplund, A., et al. (2015). Tissue-based map of the human 675 proteome. Science 347, 1260419.
676 Vento-Tormo, R., Efremova, M., Botting, R.A., Turco, M.Y., Vento-Tormo, M., Meyer, K.B., Park, 677 J.-E., Stephenson, E., Polański, K., Goncalves, A., et al. (2018). Single-cell reconstruction of the 678 early maternal–fetal interface in humans. Nature 563, 347.
679 Verreck, F.A.W., Boer, T. de, Langenberg, D.M.L., Hoeve, M.A., Kramer, M., Vaisberg, E., 680 Kastelein, R., Kolk, A., Waal-Malefyt, R. de, and Ottenhoff, T.H.M. (2004). Human IL-23- 681 producing type 1 macrophages promote but IL-10-producing type 2 macrophages subvert 682 immunity to (myco)bacteria. Proc. Natl. Acad. Sci. 101, 4560–4565.
683 Villani, A.-C., Satija, R., Reynolds, G., Sarkizova, S., Shekhar, K., Fletcher, J., Griesbeck, M., 684 Butler, A., Zheng, S., Lazo, S., et al. (2017). Single-cell RNA-seq reveals new types of human 685 blood dendritic cells, monocytes, and progenitors. Science 356, eaah4573.
686 Xing, L., Dai, Z., Jabbari, A., Cerise, J.E., Higgins, C.A., Gong, W., de Jong, A., Harel, S., 687 DeStefano, G.M., Rothman, L., et al. (2014). Alopecia areata is driven by cytotoxic T 688 lymphocytes and is reversed by JAK inhibition. Nat. Med. 20, 1043–1049.
689 Young, M.D., and Behjati, S. (2018). SoupX removes ambient RNA contamination from droplet 690 based single cell RNA sequencing data. BioRxiv 303727. bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.
691 Yun, J.H., Lee, J.Y., Kim, M.K., Seo, Y.J., Kim, M.H., Cho, K.H., Kim, M.B., Lee, W.S., Lee, 692 K.H., Kim, Y.C., et al. (2009). Clinical and Pathological Features of Generalized Granuloma 693 Annulare with Their Correlation: A Retrospective Multicenter Study in Korea. Ann. Dermatol. 21, 694 113–119.
695 Zaba, L.C., Fuentes-Duculan, J., Eungdamrong, N.J., Johnson-Huang, L.M., Nograles, K.E., 696 White, T.R., Pierson, K.C., Lentini, T., Suárez-Fariñas, M., Lowes, M.A., et al. (2010). 697 Identification of TNF-related apoptosis-inducing ligand and other molecules that distinguish 698 inflammatory from resident dendritic cells in patients with psoriasis. J. Allergy Clin. Immunol. 699 125, 1261-1268.e9.
700 Zajac, P., Islam, S., Hochgerner, H., Lönnerberg, P., and Linnarsson, S. (2013). Base 701 Preferences in Non-Templated Nucleotide Incorporation by MMLV-Derived Reverse 702 Transcriptases. PLOS ONE 8, e85270.
703 Zhang, L., Yu, X., Zheng, L., Zhang, Y., Li, Y., Fang, Q., Gao, R., Kang, B., Zhang, Q., Huang, 704 J.Y., et al. (2018). Lineage tracking reveals dynamic relationships of T cells in colorectal cancer. 705 Nature 564, 268.
706 Zhu, J., Yamane, H., and Paul, W.E. (2010). Differentiation of Effector CD4 T Cell Populations. 707 Annu. Rev. Immunol. 28, 445–489.
708
709 bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.
710 FIGURES AND FIGURE LEGENDS bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not Figure 1.certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.
A First-strand Synthesis Second-Strand Synthesis (S^3) PCR and Library Prep
TTT TTT TTT
Cytokines TSO S^3
TTT CCC-SMRT’ Cytokines TTT CCC TTT CCC-SMRT’ AAA GGG SMRT SMRT AAA SMRT SMRT TFs
TTT TTT TTT Receptors Successful Template Switching Extension + Strand Displacement
TTT Receptors TTT TTT TFs
CCC
TTT TTT TTT AAA SMRT AAA SMRT SMRT
TTT TTT TTT Failed Template Switching Transcript Recovery
B D CD4 T Cells F 80,000 1.00
60,000 0.75 10,000
40,000 0.50 5,000 UMIs per Cell Genes per Cell 20,000 0.25 Seq-Well S^3 Transcription Factors
10x V2 Cytokines Well Detection Frequency Seq− Well Cytokine Receptors 0 0 0.00 0 100,000 200,000 0.00 0.25 0.50 0.75 1.00 Seq−Well S^3 SS2 10X V2 Reads per Cell 10X Detection Frequency Technique
C E CD4 T Cells G 8,000 1.0 SmartSeq2 Seq-Well S^3 10x V2 6,000 0.5 10,000
4,000 ell − 10X) 0.0
5,000 Genes per Cell (SeqW Genes per Cell Seq-Well S^3 −0.5 2,000 Transcription Factors 10x V2 Cytokines
Difference in Gene Detection Difference Cytokine Receptors −1.0 0 0 0 100,000 200,000 0 1 2 3 3 4 5 6 Reads per Cell Average Normalized Expression log10(Reads per Cell)
H STAT6 IRF4 CCR4 TGFBR2 TNF TGFB1 5 3 3 4 4 4 2 2 3 2 3 3
2 2 2 1 1 1 Normalized 1 1 1 Gene Expression 0 0 0 0 0 0
Seq−Well S^3 10X V2 Seq−Well S^3 10X V2 Seq−Well S^3 10X V2 Seq−Well S^3 10X V2 Seq−Well S^3 10X V2 Seq−Well S^3 10X V2 CD4 T cells Grouped By Technique bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.
711 Figure 1. Overview of Second Strand Synthesis (S^3)
712 A. Conceptual illustration of the molecular features that define immune phenotypes – including 713 transcription factors, cytokines and receptors – as well as the Seq-Well second-strand synthesis 714 method (Seq-Well S^3) and how it improves detection of key genes and transcripts.
715 B. Scatterplot showing differences in per-cell transcript capture (y-axis) as a function of aligned 716 reads per cell (x-axis) between 10x Genomics v2 (10x v2, grey) and Seq-Well S^3 (black). Red 717 line indicates uniform line where transcripts per cell and aligned reads would be equivalent.
718 C. Scatterplot shows the differences in per-cell gene detection (y-axis) as a function of aligned 719 reads per cell (x-axis) between 10x v2 (grey) and Seq-Well S^3 (black).
720 D. Scatterplot comparing gene detection rates in CD4+ T cells between 10x v2 (x-axis) and Seq- 721 Well S^3 (y-axis). Black line indicates point of equivalence in gene detection frequency between 722 methods. Colors correspond to classes of genes including transcription factors (blue), cytokines 723 (magenta), and receptors (green).
724 E. Scatterplot comparing gene detection frequency (y-axis) between Seq-Well S^3 (positive 725 values) and 10x v2 (negative values) as a function of the aggregate expression levels (log(scaled 726 UMI + 1)) of an individual gene (x-axis). Black line indicates point of equivalence in gene detection 727 frequency between methods. Colors correspond to classes of genes including transcription 728 factors (blue), cytokines (magenta), and receptors (green).
729 F. Violin plot (boxplots median +- quartiles) showing the distribution of per-cell transcript capture 730 for Seq-well S^3 (blue; n = 1,485), 10x v2 (red; n = 2995), and Smart-Seq2 (black, n = 382).
731 G. Scatterplot showing the relationship between aligned reads and genes detected per cell 732 between Seq-Well S^3 (blue), 10x v2 (red) and Smart-Seq2 (black) in sorted PBMC CD4+ T cells.
733 H. Violin plots showing the distribution of normalized expression values for select transcription 734 factors, cytokines and cytokine receptors between Seq-Well S^3 and 10x v2. bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not Figure 2.certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. A Stratum Corneum Stratum Lucidum Hair follicle Stratum Granulosum
Stratum Spinosum Sebaceous gland Langerhans Cell Arrector Pili Muscle Stratum Basale Sweat gland Fibroblasts Dermis Epidermis Macrophage Mast Cell Blood vessels T Cell Nerve NK Cell Adipose tissue
Cell-Type Specific Skin Biopsy Component Cells Seq-Well S^3 Data Analysis Analysis
2. Single-cell 3. Sequencing 1. Dissociate Processing Second-Strand Synthesis & Analysis Th17 Pipeline TTT UMAP 2 UMAP 2
TTT CCC SMRT SMRT Tregs TTT UMAP 1 UMAP 1
Condition Cell Type Cluster B C 20,903 Cells 20,903 Cells
Fibroblasts
T Cells Muscle Lymphatics Sweat Gland B Cells Arterioles Venules Plasma Langerhan Neural - Schwann Dendritic
Macrophages
Hair Follicle
Keratinocytes
Mast Cells % Composition of Cell-Type Cluster by Condition % Composiition of Sample by Cell-Type Clusters 0 20 40 60 80 100 UMAP 2 UMAP 0 20 40 60 80 100
UMAP 2 UMAP UMAP 1 UMAP 1 Arterioles Hair Follicle Mast cells Plasma cells Acne GA Normal B cells Keratinocytes Macrophages Sweat Gland Alopecia Leprosy Psoriasis Dendritic Langerhans Muscle T cells Fibroblasts Lymphatic Neural-Schwann Venules bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.
735 Figure 2. Cell Types Recovered across Inflammatory Skin Conditions
736 A. (Top-Left) Diagram illustrating the anatomic organization and major features of human skin. 737 (Top-Right) Cell-type composition of the epidermis and dermis. (Bottom) Sample processing 738 pipeline used to generate a collection cellular states across skin inflammation.
739 B. (Left) UMAP plot for 20,308 cells colored by cell-type cluster. (Right) Stacked barplot showing 740 the cell-type composition for each of the nine skin biopsies.
741 C. (Left) UMAP plot for 20,308 cells colored by inflammatory skin condition. (Right) Stacked 742 barplot showing the proportion of cells from each skin condition within phenotypic clusters. bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not Figure 3. certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Condition A 2,908 T cells B 2,908 T cells T cell Sub-cluster
0 1 8 Acne 0 2 Alopecia 6 3 GA FR 2 FR 2 4 2 Leprosy 7 5 1 Normal 6 Psoriasis 4 Proportion of Total T cells
7 % Composition by Condition 3 5 8 0 20 40 60 80 100
FR 1 FR 1 0 20 40 60 80 100
C D 0 1 2 3 4 5 6 7 8 CD4 CD8A EOMES CCL5 CCL5 CD8A CCL4 HSPA1B HSPA6 IL7R FOXP1 CD55 ANKRD36C NKTR RGCC AIM1 FOXP3 RORC FOXP3 GNLY NR4A1 IKZF2 TBC1D4 RBPJ CRADD TRPS1 BHLHE40 PDE4D PTPN13 CXCL13 XCL1 GNLY GZMB FR 2 Gene Expression Level Gene Expression Level FR 1 0 6 0 1 2 3 4 5
E T cell Sub-cluster F Condition G TRAC TRBC TRAV TRBV ● ● ● CD8A EOMES CD55 IFNG ● ● IL21R IL2RA TBX21 ● IER5 IRF1 ● IL23A ● IL32 PDE4D ● IL16 NR3C1 FOXP1 Detection Rate ● CD40LG IRAK1 ● CCR7 STAT5B XCL1 RORA 20 40 60● 80 PNISR PTP4A1 ZNF638 ● DOCK8 CCL4 ● FOSB KLF6 KLF9 <5,000 5,000-25,000 25,000-100,000 >100,000 FOS TNFRSF9 TSC22D3 Aligned Reads per Cell FOXP3 FASLG H BATF Sub-Groupings of T cell Sub-Cluster 8 TIGIT SLAMF7 LAIR2 CD3D CCR8 CCL5 CD8A RBPJ CD5 BHLHE40 LITAF S100A6 RORC S100A10 GBP5 STAT5A TNFSF8 CLEC2D IL12RB2 STAT3 SLAMF1 PDE4D TRGC2 TGFBR3 AIM1 NKG7 CXCR6 TYROBP PDE4A EZR CCL3 XCL1 CCL4 IL7R GNLY XCL2 GZMH TRDC HSPA6 IRF8 TRGC1 IL2RA KIT KLRD1 HSPA1B KLRB1 TNFSF11 KLRC2 TNFRSF18 DNAJB1 TNFSF4 KLRF1 Acne Alopecia GA Leprosy Normal Psoriasis 0 1 2 3 4 5 6 7 8 TNFRSF4 IL1R1 LACTB P4HB Gene Detection Frequency TNIP1 Row-Normalized Expression Levels 0 1.0 Gene Expression Level −2 2 0 5 bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.
743 Figure 3. Identification of Inflammatory T cell States using Seq-Well S^3
744 A. (Left) Force-directed graph (Fruchterman Reingold) of 2,908 T cells colored by the nine 745 phenotypic sub-clusters identified by Louvain clustering. (Right) Stacked barplots showing the 746 distribution of these T cell sub-clusters within each skin biopsy.
747 B. (Left) Force-directed graph of 2,908 T cells colored by inflammatory skin condition. (Right) 748 Stacked barplots showing the contribution of each inflammatory skin condition to the T cell sub- 749 clusters.
750 C. T cell force-directed graphs displaying normalized expression (log(scaled UMI + 1)) of a 751 curated group of sub-cluster-defining gene. Higher expression values are shown in black.
752 D. Heatmap showing normalized gene expression values (log(scaled UMI + 1)) for a curated list 753 of sub-cluster-defining genes across nine T cell sub-clusters.
754 E. Heatmap showing the rate of detection for lineage-defining transcription factors, cytokines, and 755 cytokine receptors across T cell phenotypic clusters.
756 F. Heatmap showing average expression of genes enriched across T cells by inflammatory skin 757 condition (row-normalized average expression values).
758 G. Plot showing rates of detection of TCR genes from human skin T cells across a range of 759 sequencing depths.
760 H. Heatmap showing normalized gene expression values (log(scaled UMI + 1)) for genes enriched 761 in sub-group analysis of T cell sub-cluster 8. bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not Figure 4. certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. A 2,371 Cells Condition B 2,371 Cells Myeloid Sub-cluster
Acne Alopecia GA Dendritic Cells Langerhans Leprosy Macrophage 1 Normal Psoriasis Macrophage 2 Mast % Composition by Condition % Composiition of Myeloid Clusters 0 20 40 60 80 100 0 20 40 60 80 100
CD207 CD163 IL8 C D Dendritic Langerhans Mphage 1 Mphage 2 Mast
IL8 CCR7 EREG CXCL3 INHBA HLA−DQB2 CD207 TPSAB1 LYZ CLEC9A CD1A
CLDN1 LYZ APOE CHIT1 CHI3L1 LIPA
CPA3 CHIT1 EREG CD163 F13A1 STAB1
TPSAB1 CPA3 IL1RL1
HDC
FR 2 Gene Expression Level CMA1 FR 1 0 6 Gene Expression Level 0 6
Dendritic Cell Sub-Groupings E Langerhans Cells F Mast Cell Expression G SOD2 40 HDC IL8 HLA−B HLA−A CCL3 INHBA HPGD IL1RN EREG CXCL3 CPA3 TNFRSF1B 30 CCL4 CXCL2 CMA1 PTGS2 EIF4A3 B2M FCGR2A TPSAB1 FCGR2B FAM46A FOS 20 TPSG1 CH25H MS4A6A CTSS FCER1A IDO1 FOSB (Adjusted P-Value) TPSD1 AMICA1 10 HLA−F CD300A HLA−C DSG2 CKLF ATP1B1 PRSS27 WDFY4
-log 10 STAT1 CLEC9A NFKBIA C1orf21 TMEM14C IFI30 DNASE1L3 SLC1A5 SERPINB9 SCG2 IRF8 RPS5 CD1E CD1B CCL2 SOCS2
Leprosy Normal Psoriasis 1 Alopecia Psoriasis 2 GA 2 Acne 1 Acne 2 SLCO5A1 0 LAMP3 LIMCH1 IRF4 -1 0 1 CCL17 CCL22 Average Log fold Change Dermal DC 1 Dermal DC 2 Dermal DC 3 cDC1 cDC2 Gene Expression Level 0 4
Gene Detection Frequency 0 1.0 bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.
762 Figure 4. Diverse Myeloid Cell States Uncovered using Seq-Well S^3
763 A. (Left) Force-directed graph of 2,371 myeloid cells colored by five phenotypic sub-clusters (NB, 764 Langerhans cells were enriched from leprosy and normal skin). (Right) Stacked barplots showing 765 the distribution of myeloid sub-clusters within each skin biopsy.
766 B. (Left) Force-directed graph of 2,371 myeloid cells colored by inflammatory skin condition. 767 (Right) Stacked barplots showing the contribution of each inflammatory skin condition to each 768 myeloid sub-cluster.
769 C. Force-directed graphs of 2,371 myeloid cells that highlighting expression of a curated group of 770 sub-cluster defining genes (log(scaled UMI + 1)).
771 D. Heatmap showing the normalized expression (log(scaled UMI + 1)) of a curated list of myeloid 772 cell-type cluster-defining genes.
773 E. Volcano plot showing genes differentially expressed in Langerhans cells between leprosy (ncells
774 = 56) and normal skin (ncells = 120). Log10-fold change values are shown on the x-axis and -log10 775 adjusted p-values are shown on the y-axis.
776 F. Heatmaps showing the normalized expression (log(scaled UMI + 1)) of mast-cell proteases 777 across inflammatory skin conditions.
778 G. Heatmap showing detection frequencies for transcription factors, surface receptors, and 779 cytokines across DC sub-populations. bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under Figure 5. aCC-BY-NC-ND 4.0 International license.
A B C Stratum corneum Stratum lucidum Stratum granulosum
Stratum spinosum UMAP 2 Stratum basale
UMAP 1 Acne Alopecia GA Leprosy Normal Psoriasis
D E Normal Keratinocyte Differentiation Trajectory KRT14
KRT14
Terminal FLG Basal
Basal Differentiating Terminal
FLG
SPRR2E F APOBEC3A G FOSL IL36G TNFAIP3 APOBEC3 KRT16 SPRR2A CDKN1A FOSL1 CCL20 RNF144B
KRT6B Normal NDRG1 KRT6C NFKBIA EIF4A3 DDIT4 KLF6 HCAR3 PNRC1 ZNF217 ARL14 Psoriatic S100A9
Genes SLC26A2 PLXNA2 SERF2 Normal Uninvolved Psoriatic USP54 H DEGS1 IL-17R ALDOC C14orf132 DAPI RAI14 DDAH2 AP1B1 SCP2 PLEKHA5 MLEC COX6B1 CFD ACAT2 SREBF1 TLCD1 PSAT1 IL33 -0.6 -0.3 0 0.3 0.6 Pseudotime Correlation Values Normal Psoriasis bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.
780 Figure 5. Keratinocyte Differentiation Trajectories
781 A. Diagram showing the layers of the epidermis and morphologic changes associated with 782 keratinocyte differentiation.
783 B. UMAP embedding of 20,308 cells with all keratinocyte and hair follicle populations highlighted 784 in blue.
785 C. Diffusion map of 5,141 keratinocytes colored by inflammatory skin condition. Axes correspond 786 to diffusion components.
787 D. t-SNE plot showing differentiation trajectory of keratinocytes from normal skin from basal cells 788 (yellow) through differentiating cells (aqua) and terminal keratinocytes (purple).
789 E. (Top-left) tSNE plot of normal keratinocytes with normalized KRT14 expression values 790 overlayed. (Top-right) Immunohistochemistry staining showing the expression of KRT14 from the 791 human protein atlas (Uhlén et al., 2015). (Bottom-left) tSNE plot of normal keratinocytes with 792 normalized FLG expression values overlayed. (Bottom-right) Immunohistochemistry staining of 793 FLG from the human protein atlas(Uhlén et al., 2015). Scale bars = 50 microns.
794 F. Stacked barplot showing genes with the highest differential pseudo-time correlation between 795 normal keratinocytes (blue) and psoriatic keratinocytes (red) sorted by correlation values in 796 psoriatic keratinocytes. Correlation values shown on the x-axis represent Pearson correlation 797 coefficients between normalized gene expression and diffusion pseudotime.
798 G. (Top) Immunofluorescence staining in normal (above) and psoriatic (below) for FOSL, IL36G, 799 TNFAIP3, and APOBEC3. All images stained for nuclei (DAPI) and gene of interest (Red 800 Fluorescence). Scale bar = 100 microns.
801 H. Immunofluorescence staining for IL-17R expression (green) in normal (left), uninvolved 802 (middle), and psoriatic skin (right). Scale bar = 100 microns. bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under Figure S1. aCC-BY-NC-ND 4.0 International license.
A 1. mRNA Capture 2. First Strand Synthesis 3. Template Switching
Successful Template Switching Failed Template Switching
TTT TTT TTT TTT
TTT TTT CCC TTT CCC TTT AAA AAA GGG-SMART AAA
TTT TTT TTT TTT
Cell UMI Barcode
4. Denature RNA Template 5. Second Strand Synthesis
TTT TTT TTT
TTT TTT TTT NNN SMART 0.1M NaOH
TTT TTT AAA TTT
B C D Library Complexity - Cell Lines Library Complexity - Human PBMCs Transcript Purity in Species Mixing Seq−Well V1 Human (m=0.22) SeqWell V1 hPBMCs(m=0.03) Seq-Well Protocol V1 Seq−Well V1 Mouse (m=0.21) SeqWell S^3 hPBMCs (m=0.30) Seq−Well S^3 Human (m=0.63) 10X V2 hPBMCs (m=0.18) Seq−Well S^3 Mouse (m=0.67) 200
150 Transcripts Transcripts
100
Mouse Human 50 Nummber of Cells (97.8%) (97.8%) 0 50,000 100,000 150,000 200,000 0 50,000 100,000 150,000 200,000 0.1 0.9 0 50,000 100,000 150,000 200,000 0 50,000 100,000 150,000 200,000 Reads 0 Reads 0.0 0.25 0.50 0.75 1.0
Optimization - Cell Lines Optimization - Human PBMCs Seq-Well Protocol S^3 Seq−Well V1 Normal (m=0.036) 300 Seq−Well V1 Normal (m=0.22) Seq−Well V1 ExoHeat (m=0.027) Seq−Well V1 ExoHeat (m=0.19) Seq−Well S^3 ExoHeat (m=0.34) Seq−Well S^3 ExoHeat (m=0.75) Seq−Well S^3 No TSO (m=0.32) Seq−Well S^3 No TSO (m=0.68) Seq−Well S^3 Normal (m=0.39) SeqWell S^3 Normal (m=0.63)
200 Transcripts Transcripts 100 Mouse Human Nummber of Cells (97.4%) (97.6%)
0.1 0.9 0 50,000 100,000 150,000 200,000 0 50,000 100,000 150,000 200,000 0
0 50,000 100,000 150,000 200,000 0 50,000 100,000 150,000 200,000 0.0 0.25 0.50 0.75 1.0 Reads Reads Fraction of Transcripts Uniquely Mapped to Human bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.
803 Supplementary Figure 1. Second-Strand Synthesis Overview, related to Figure 1
804 A. Illustration of the second strand synthesis procedure: (1) mRNA is captured via poly-T priming 805 of poly-adenylated mRNA; (2) First strand synthesis is performed to generate single-stranded 806 cDNA template on bead-bound sequences; (3) Successful template switching: The use of 807 enzymes with terminal transferase activity generates a 3’ overhang of 3 cytosines. Template 808 switching utilizes this overhang to append the SMART sequence to both ends of the cDNA 809 molecule during first strand synthesis. Failed Template Switching: If template switching fails, this 810 results in loss of previously primed and reverse transcribed mRNA molecules; (4) mRNA template 811 is chemically denatured using 0.1M NaOH; (5) Second strand synthesis is performed using a 812 random-octamer with the SMART sequence in the 5’ orientation; and, (6) Following second strand 813 synthesis, PCR amplification, library preparation and sequencing are performed to generate data.
814 B. Scatterplots show the relationship between transcript detection (y-axis) and number of aligned 815 reads per cell (x-axis) for an initial experiment (top) series of optimization conditions using 816 HEK293 and NIH-3T3 cell lines (botttom).
817 C. Scatterplots that illustrate the relationship between number of transcripts detected (y-axis) and 818 number of aligned reads per cell (x-axis) between Seq-Well V1 and Seq-Well S^3 in sequencing 819 experiments for an initial experiment (top) and a series of optimization experiment using human 820 PBMCs (bottom).
821 D. Histograms that show the fraction of transcripts uniquely mapped to the human genome for 822 each cell for Seq-Well V1 (Top) and Seq-Well S^3 (Bottom). Colors indicate species classification 823 for cells with at least 90% purity of human (blue) or mouse (red) mapping. bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under Figure S2. aCC-BY-NC-ND 4.0 International license.
A B C
Seq-Well S^3 10x Genomics v2 7.5
5.0
2.5 Genes / Cell
0 t−SNE 2 t−SNE 2 5.0
Percentage 4.5
4.0
3.5 (UMI / Cell) 10 3.0
t−SNE 1 t−SNE 1 log 0 20 40 60 80 100 2.5 Seq-Well S^3 10x Genomics v2 B Cells - S^3B Cells - 10xCD4 - S^3 CD4 - 10x CD8/NK - S^3CD8/NK - 10xMonocytes-Monocytes- S^3 10x B Cells Monocytes CD4 T Cells CD8/NK Cells
Cell Type by Technique
D B Cells CD4 T Cells CD8/NK Cells Monocytes 1.00
0.75
0.50
Frequency 0.25
Well S^3 Detection Seq− Well 0.00 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 0.25 0.50 0.75 1.00 10X v2 Detection Frequency
Transcription Factors Cytokines Cytokine Receptors E B Cells CD4 T Cells CD8/NK Cells Monocytes 1.0
0.5
0.0
ell S^3 − 10X v2) −0.5
(SeqW −1.0
Difference in Gene Detection Difference 0 1 2 3 0 1 2 3 0 1 2 3 0 1 2 3
Average log10 Gene Expression Transcription Factors Cytokines Cytokine Receptors F 1.00 1.00 1.00
0.75 0.75 0.75
0.50 0.50 0.50
0.25 0.25 0.25 10X v2 Detection Frequency Well S^3 Detection Frequency Seq− Well S^3 Detection Frequency Seq− Well
0.00 0.00 0.00
0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 Smart−Seq2 Detection Frequency 10X v2 Detection Frequency Smart−Seq2 Detection Frequency bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.
824 Supplementary Figure 2. PBMC Methods Comparisons, related to Figure 1
825 A. t-SNE plot showing detected cell-types among PBMCs including CD4+ T cells (green), 826 CD8+/NK Cells (blue), B cells (purple), and Monocytes (red) using 10X v2 and Seq-Well S^3. Cells 827 recovered using Seq-well are colored with darker shades.
828 B. Stacked barplots show the proportion of cell types recovered using Seq-Well S^3 (left) and 829 10X v2 (right).
830 C. Top: Violin plots (boxplots median +- quartiles) showing the distribution of per cell gene 831 detection from Seq-Well S^3 (left) and 10X v2 (right). Bottom: Violin plots (boxplots median +/- 832 quartiles) showing the distribution of per cell-gene detection from Seq-Well S^3 (left) and 10X v2 833 (right).
834 D. Scatterplots showing a comparison of gene detection frequencies between Seq-Well S^3 (y- 835 axis) and 10x v2 (x-axis) for each cell type.
836 E. Scatterplots showing the difference in gene detection between Seq-Well S^3 and 10X v2 (y- 837 axis) as a function of average normalized expression (log(scaled UMI + 1)) (x-axis).
838 F. Scatterplots showing a comparison of gene detection frequencies among sorted CD4+ T cells 839 between (Left) Seq-well S^3 (y-axis) and 10x v2 (x-axis), (Middle) Seq-Well S^3 (y-axis) and 840 Smart-Seq2 (x-axis), and (Right) 10x v2 (y-axis) and Smart-Seq2 (x-axis). bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not Figure S3.certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Height 20,308 Cells 0 10 20 30 40 50 A ● Arteriole−1 ● Keratinocyte−8 C ● Arteriole−2 ● Langerhans Fibroblasts ● Bcells ● Lymphatic Keratinocyte−3 T cells ● ● Dendritic Macrophage Keratinocyte−8 Muscle Lymphatic ● Fibroblast−1 ● Mast ● Fibroblast−2 ● Muscle Keratinocyte−2 B cells Arterioles Venules Secretory ● Fibroblast−3 ● Neural-Schwann Langerhans Keratinocyte−6 ● Fibroblast−4 ● Secretory Neural-Schwann Dendritic Keratinocytes ● Fibroblast−5 ● Tcell−1 Keratinocyte−5 UMAP2 ● ● Macrophage HairFollicle Tcell−2 Keratinocyte−1 Hair Follicle ● Keratinocyte−1 ● Tcell−3 ● Keratinocyte−2 ● Tcell−4 Keratinocyte−4 ● ● Keratinocyte−3 Venule−1 Fibroblast−4 ● Keratinocyte−4 ● Venule−2 ● Keratinocyte−5 ● Venule−3 Fibroblast−1 Mast ● Keratinocyte−6 ● Venule−4 Fibroblast−2 ● Keratinocyte−7 UMAP1 Fibroblast−3 TAGLN MYLK Fibroblast−5 B MS4A1 BANK1 CD79A Macrophage HLA−DRB1 HLA−DPB1 Dendritic LYZ CLEC10A DCN B cells COL6A2 COL6A1 Langerhans MFAP4 COL3A1 COL1A1 Venule−3 MMP2 KRT5 KRT14 Venule−2 DSC3 KRT1 Venule−1 DSG1 S100A9 Venule−4 KRT6A SPINK5 LY6D Arteriole−1 MKI67 SLURP1 Arteriole−2 CD207 CCL21 Lymphatic LYVE1 CTSS Neural-Schwann TPSAB1 CPA3 HairFollicle NEAT1 KCNQ1OT1 SCN7A Keratinocyte−7 DCD Muscle IGHG1 TRBC2 Secretory CD8A TIGIT Mast SELE DARC Tcell−2 Arteriole B cellsDendritic Fibroblasts Hair Follicle Keratinocytes LangerhansLymphaticsMacrophagesMast cellsMuscleNeural-SchwannSecretory T cells Venules CD93 Tcell−3 Tcell−1
Gene Expression Levels Tcell−4 0 6 D E Acne 1 - 4,874 Cells Acne 2 - 343 Cells Alopecia - 1,335 Cells
4.0
3.5 (Genes)
10 3.0 log
GA 1 - 1,352 Cells GA 2 - 2,112 Cells Leprosy - 3,929 Cells 5.0 4.5
(UMI) 4.0 10
log 3.5
3.0
6.0 Psoriasis 1 - 1,759 Cells Psoriasis 2 - 1,819 Cells Normal - 2,785 Cells 5.0 (Reads) 10 4.0 log
3.0
Arteriole B DendriticFibro HairFollicleKC LC LymphaticMast MphageMuscle Neural/SchwannPlasma SweatGlandT Venule tSNE 2 tSNE 1 bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.
841 Supplementary Figure 3. Overview of Samples, related to Figure 2
842 A. UMAP plot for 20,308 cells colored by 33 cell type cell type clusters (Louvain Resolution: 2.0).
843 B. Heatmap showing the relative expression of cell-type defining gene signatures across 20,308 844 cells.
845 C. Dendrogram of hierarchical clustering shows similarity of cell type clusters among top 25 846 cluster-defining genes (Figure S3B).
847 D. t-SNE plots for each of the nine skin biopsies colored by generic cell type.
848 E. Violin plots show the distribution of per-cell quality metrics displayed in UMAP embedding of 849 20,308 cells colored by colored generic cell-type classification (Figure 2B). bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not Figurecertified S4. by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under TCR Detection Frequencies in PBMCs aCC-BY-NC-NDT Cell 4.0 Sub-ClusterInternational 0 license. 2,908 T Cells C ● A B ● ● ICOS ● ● IFNG ● 0 IER5 1 NR4A1 8 HSPH1 ● 0 2 DNAJB1 5 6 CCDC64 ● 3 Detection Rate STAT4 4 3 FR 2 4 STAT5B ● 2 CCR4 2 7 5 CCR5 1 ● ● 1 IRAK1 6 ● 4 XCL1 0 ●● 7 Cell Sub-Cluster T ● 3 XCL2 0 20 40 60 80 100 5 8 TNF EGR2 KLF9 Seq-Well v1 10x v2 Seq-Well S^3 FR 1 CCL3 FASLG TRAC TRBC TRAV TRBV T cell Savant Signature Scores Paired TRAC-TRBC T Cell Sub-Cluster 8 IMGN_T_4FP3+25+_Sp WRS_Regulatory_T−cell CD3D TCR-V/J Detection Frequencies CD8A TH_Th1_Harvard CD5 0.08 S100A6 TH_Th2_Harvard S100A10 TNFSF8 TH_Th17 CLEC2D SLAMF1 5 0.06 IMGN_T_4Nve_Sp TRGC2 NKG7 4 IMGN_T_8Eff_Sp_OT1_12hr_LisOva TYROBP CCL3 3 IMGN_T_8Mem_LN CCL4 GNLY 2 0.04 WRS_NK_cell GZMH HPCA_NK_cells IRF8 1 IL2RA HBA_CD56+_NKCells KIT 0 TNFSF11 0.02 WRS_NK_T−cell TNFRSF18 TNFSF4 Detection Frequency Seq− Well 0 1 2 3 4 5 6 7 8 TNFRSF4 IL1R1 LACTB 0.00 P4HB TNIP1 0.00 0.02 0.04 0.06 0.08 −2 −1 0 1 2 10X Detection Frequency
2,371 Myeloid Cells D E Myeloid Savant Signature Scores Signature HPCA_Dendritic_cells_BDCA1+ Score 0 HPCA_Dendritic_cells_BDCA3+ IMGN_DC_LC_Sk 1.5 1 1 MBA_mast_cells 2 Dendritic Cells MBA_mast_cells_IgE 0.5 Langerhans 0 3 HPCA_M_IFNa Macrophage 1 HPCA_M1_IFNg −0.5 4 Macrophage 2 HPCA_M2_IL−4 −1 5 Mast HPCA_Macrophage −1.5 6 LangerhanDendriticMast Macrophage1Macrophage2 7 8
Condition DC Cluster F 502 Dendritic Cells G 502 Dendritic Cells H Villani et al. Signatures DC1 DC2 Dermal DC 1 DC3 Dermal DC 2 DC4 Dermal DC 3 DC5 UMAP 2 cDC1 UMAP 2 DC6 cDC2 DermalDermal DC 1 DermalDC 2 cDC1 DC 3 cDC2 % Composition by Condition % Composiition of DC Clusters 0 20 40 60 80 100
UMAP 1 0 20 40 60 80 100 UMAP 1 I 0 0.2 0.4 0.6 0.8 1 CD44 J IL8 CLEC10A EREG FCER1A IL12B CCL3 INHBA EREG CXCL3 CCL4 CXCL2 PTGS2 FCGR2A FCGR2B NR4A1 MS4A6A CCL17 FCER1A CLEC9A SOCS2 IRF4 AMICA1 CD300A WDFY4 CLEC9A DNASE1L3 IRF8 CD1E SOCS2 SLCO5A1 LAMP3 IRF4 CCL17 Gene Expression Level CCL22 Gene Expression Level 0 1 2 3 4 5
0 1 2 3 4 5 6 bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.
850 Supplementary Figure 4. Immune Cell Heterogeneity, related to Figures 3 and 4
851 A. (Top) Force-directed graph of 2,903 T cells colored by T cell sub-cluster. (Bottom) Heatmap 852 of gene-set enrichment scores based on comparison of T cell phenotypic sub-clusters to a curated 853 list of reference signatures in the Savant database.
854 B. Sub-grouping results for (top) T cell sub-cluster 0 and (bottom) T cell sub-cluster 8. For each 855 analysis, t-SNE plots colored by inflammatory skin condition (top-left) and sub-cluster (bottom- 856 left) are shown. For each clusters, heatmaps show gene expression patterns across T and NK 857 cells sub-types (right).
858 C. (Top) Detection rates for TCR genes for PBMCs in Seq-Well v1, 10x v2. and Seq-Well S^3. 859 (Bottom) Detection frequency of TCR V-J (e.g. TRAV/J and TRBV/J) genes in CD4+ T cells from 860 peripheral blood between Seq-Well S^3 (y-axis) and 10x v2 (x-axis). Colors correspond to TRAJ 861 (red), TRAV (green), TRBJ (blue), and TRBV (purple) genes.
862 D. Force-directed graph of 2,371 myeloid cells colored by myeloid phenotypic sub-clusters.
863 E. Heatmap of gene-set enrichment scores based on comparison of myeloid phenotypic sub- 864 clusters to a curated list of reference signatures in the Savant database.
865 F. (Left) UMAP plot for 502 dendritic cells from human skin colored by phenotypic sub-grouping. 866 (Right) Stacked barplot showing composition of dendritic cells within each of nine skin biopsies 867 by DC sub-cluster.
868 G. (Left) UMAP plot for 502 dendritic cells from human skin colored by inflammatory skin 869 condition. (Right) Stacked barplot showing contribution of inflammatory skin conditions to each 870 dendritic cell sub-grouping.
871 H. Heatmap showing average signature score across 5 dermal DC populations based on dendritic 872 cell signatures from Villani et al. Science 2017.
873 I. Heatmap showing the distribution of normalized gene expression levels (log(scaled UMI + 1)) 874 for cluster-defining genes across dermal DC subpopulations.
875 J. UMAP plots colored by normalized expression levels for DC sub-grouping-defining genes. bioRxiv preprint doi: https://doi.org/10.1101/689273; thisCondition version posted July 2, 2019. The copyright holder for this preprint (which was not Figure S5.certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available underEndothelial Cluster aCC-BY-NC-ND 4.0 International license. A B
Arteriole 1 Venules Acne Arteriole 2 Alopecia Lymphatic Lymphatic GA FR 2 FR 2 Venule 1 Leprosy Venule 2 Normal Arterioles Venule 3 Psoriasis
Venule 4 % Composition by Condition Proportion of Total Endothelial Cells 0 20 40 60 80 100 FR 1 FR 1 0 20 40 60 80 100
ITGA10 CD93 ICAM2 C D E ICAM4 TAGLN ICAM1 MYH11 ITGA5 NOTCH3 ITGA6 MT1A SELE ITGAE COL3A1 ITGA2 COL1A2 DARC ITGB6 CCL21 VCAM1 MMRN1 ICAM3 LYVE1 LYVE1 MADCAM1 ITGB7 LYVE1 SELE ITGB4 VWA1 ITGA9 RAMP3 ITGB2 DARC ITGB8 CD93 ITGAX PLVAP ITGA4 HLA−DRA ITGAD ITGB1 SEMA3G ITGA3 IGFBP3 TAGLN ITGA8 CXCL2 ITGA1 CXCL3 TAGLN CXCL12 ITGB5 TUBA1B ITGA7 ITGA11 ICAM5 MKI67 Arteriole1Arteriole2LymphaticVenule1Venule2Venule3Venule4
Gene Expression Level 0 8 Gene Expression Level Row-Normalized Gene Expression 0 5 −2 2
Condition Fibroblast Cluster F G
0 2 Acne 6 1 4 Alopecia 3 2 5 GA
FR 2 3 FR 2 4 Leprosy 1 5 Normal 6 Psoriasis 0 % Composition by Condition Proportion of Total Fibroblasts
FR 1 FR 1 0 20 40 60 80 100 0 20 40 60 80 100
LRRC32 LTBP4 DPEP1 H I COL1A1 COL1A2 COL3A1 PI16 ITIH5 ELN MT1E CCL19 LTBP4 OGN IGFBP5 CRLF1 ELN COL11A1 IGF1 CRLF1 DUSP4 GALNT15 SDC4 LRRC32 COMP MMP3 COL18A1 SFRP2 ADAMDEC1 STEAP4 MT1E PI16 TGFBI HLA−DRB1
DPEP1 COL11A1
FGFBP2
Gene Expression Level Gene Expression Level 0 FR 2 7 0 5 FR 1 bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.
876 Figure S5. Stromal Cell Diversity
877 A. Force-directed plots for 4,996 endothelial cells colored by phenotypic sub-cluster (left) and 878 stacked barplot showing the distribution of endothelial phenotypic sub-clusters across samples 879 (right).
880 B. Force-directed plots for 4,996 endothelial colored by inflammatory skin condition (left) and 881 stacked barplot showing the contribution of each inflammatory skin condition to endothelial 882 phenotypic sub-clusters.
883 C. Forced-directed plot colored by normalized expression level of genes that mark endothelial cell 884 types: (Left) CD93, venules, (Middle) TAGLN, arterioles, (Right) LYVE1, lymphatics.
885 D. Heatmap showing patterns of normalized gene expression levels (log(scaled UMI + 1)) across 886 7 clusters of endothelial cells.
887 E. Heatmap showing row-normalized expression levels of vascular addressins across phenotypic 888 sub-clusters of endothelial cells.
889 F. Force-directed plots for 4,189 fibroblasts colored by phenotypic sub-cluster (left) and stacked 890 barplot showing the distribution of fibroblast phenotypic sub-clusters across samples (right).
891 G. Force-directed plots for 4,189 fibroblasts colored by inflammatory skin condition (left) and 892 stacked barplot showing the contribution of each inflammatory skin condition to fibroblast 893 phenotypic sub-clusters.
894 H. Force-directed graphs highlighting fibroblast cluster defining genes.
895 I. Heatmap showing the normalized gene expression levels (log(scaled UMI + 1)) of fibroblast 896 cluster-defining genes. bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under Figure S6. aCC-BY-NC-ND 4.0 International license.
A Normal Skin Psoriatic Skin
SLC3A2 ATF3 LIMA1 JUN DSG3 DNAJB1 BNC1 GADD45B MT2A NR4A1 STMN1 FOSB NCOA7 HSPA6 CDHR1 FTL CXCL14 HSPH1 ITGA6 DNAJA1 CAV1 HSP90AA1 KRT14 SQSTM1 KRT5 S100A2 COL17A1 KRT5 DST ATP1A1 S100A2 S100A6 KRT15 APCDD1 COL7A1 TUBB LAMB3 SOD1 CALML3 MT−ATP6 CASP14 MT−CO1 CA2 MT−ND2 CMTM6 MT−CYB CALML5 MT−ND5 Gene Expression SPINK5 MALAT1 SCEL TACSTD2 CSTA LYPD3 Level SBSN NDRG1 TMEM45A KLF6 IVL CD55 CLDN4 HBEGF 1.00 SPRR1A LDLR SPRR1B IER3 SERPINB2 NFKBIA FGFBP1 PHLDA2 PTPN21 ADM 0.75 GSN DDIT4 KIF1C HSPA5 NCCRP1 IDI1 LY6G6C POF1B 0.50 POF1B APOBEC3A SLURP1 SERPINB2 KLK7 IL1RN SERPINA12 ITPKC HOPX TNFAIP3 0.25 KRT77 ELF3 CTD−2319I12.1 LGALSL C1orf68 RHCG LYNX1 SLC19A2 F3 CCL20 0.00 BLMH HSPB8 TPRG1 SPRR2D SPTLC3 SPRR2E ELOVL6 IL36G ID4 SPRR2A ELOVL1 CLDN4 GPRIN2 IL36RN GAN PI3 RHOD EREG AVPI1 IVL KRT78 SPRR1B CNFN SPRR1A DHCR7 KRT17 TGM1 CTD−2319I12.1 C15orf48 SPINK5 SNCG SCEL SH3KBP1 DSC1 KLK5 KRT2 SERPINB7 CSTA CARD18 CALML5 SNTB1 SLPI ATP1B1 SBSN MBOAT2 KRT6C CPM CALML3 EMB S100A7
Basal Differentiating Terminal Basal Differentiating Terminal
B C Normal Psoriasis IL-17A 0.50 0.25 Terminal 0.00 −0.25
IL-17A + TNF Terminal 0.50
Basal Basal 0.25 0.00 −0.25
Basal Differentiating Terminal TNF 0.4
0.2 FLG KRT5 0.0
Terminal IFNA 0.75 0.50 Terminal 0.25 0.00 Basal −0.25
Basal IL-4 0.2 0.1 Expression 0.0 0 6
Cytokine Response Signature Score −0.1
KRT14 IL36G IL-13 0.2 0.1 0.0
Terminal −0.1
IFNG Terminal 0.4 Basal 0.2
Basal 0.0 −0.2
Expression Normal Normal Psoriasis 0 6 Basal Differentiating Terminal bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.
897 Supplementary Figure 6. Keratinocyte Differentiation Trajectories, related to Figure 5
898 A. (Left) Heatmap showing enrichment of genes along pseudo-temporal trajectories for normal 899 keratinocytes. (Right) Heatmap showing enrichment of genes along pseudo-temporal trajectories 900 among psoriatic keratinocytes.
901 B. Differentiation trajectories for Normal (left) and Psoriatic (right) keratinocytes.
902 C. Violin plots showing localization of cytokine response signatures in basal, differentiating and 903 terminal keratinocytes for Normal (left) and Psoriatic (right) keratinocytes.