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Hypr-Seq: Single-Cell Quantification of Chosen Rnas Via Hybridization and Sequencing of DNA Probes

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Hypr-Seq: Single-Cell Quantification of Chosen Rnas Via Hybridization and Sequencing of DNA Probes HyPR-seq: Single-cell quantification of chosen RNAs via hybridization and sequencing of DNA probes Jamie L. Marshalla,1, Benjamin R. Doughtya,1, Vidya Subramaniana, Philine Guckelbergera,b, Qingbo Wanga,c,d, Linlin M. Chena, Samuel G. Rodriquesa,e,f,2, Kaite Zhanga, Charles P. Fulcoa,3, Joseph Nassera, Elizabeth J. Grinkevicha, Teia Noela, Sarah Mangiamelia, Drew T. Bergmana, Anna Grekaa,g, Eric S. Landera,h,i,4,5, Fei Chena,4,5, and Jesse M. Engreitza,j,k,4,5 aBroad Institute of MIT and Harvard, Cambridge, MA 02142; bDepartment of Biology, Chemistry, and Pharmacy, Freie Universität Berlin, 14195 Berlin, Germany; cAnalytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114; dProgram in Bioinformatics and Integrative Genomics, Harvard Medical School, Boston, MA 02115; eDepartment of Physics, Massachusetts Institute of Technology, Cambridge, MA 02139; fMIT Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139; gDepartment of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115; hDepartment of Systems Biology, Harvard Medical School, Boston, MA 02115; iDepartment of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139; jDepartment of Genetics, Stanford University School of Medicine, Stanford, CA 94305; and kBasic Science and Engineering Initiative, Betty Irene Moore Children’s Heart Center, Lucile Packard Children’s Hospital, Stanford University School of Medicine, Stanford, CA 94305 Contributed by Eric S. Lander, November 9, 2020 (sent for review May 29, 2020; reviewed by Alexander Meissner, Kathrin Plath, and Chun J. Ye) Single-cell quantification of RNAs is important for understanding and quantify specific transcripts in single cells, for example using cellular heterogeneity and gene regulation, yet current approaches target-specific reverse transcription of RNA during library prep- suffer from low sensitivity for individual transcripts, limiting their aration (13–15) or hybrid selection, PCR, or linear amplification utility for many applications. Here we present Hybridization of on single-cell cDNA libraries (16–19).However,eachofthese Probes to RNA for sequencing (HyPR-seq), a method to sensitively approaches has certain limitations, such as the number of tran- quantify the expression of hundreds of chosen genes in single cells. scripts that can be measured, the inability to detect non- HyPR-seq involves hybridizing DNA probes to RNA, distributing cells into nanoliter droplets, amplifying the probes with PCR, and se- polyadenylated transcripts, or the feasibility of profiling very large quencing the amplicons to quantify the expression of chosen genes. numbers of cells (SI Appendix, Note S1). New approaches are HyPR-seq achieves high sensitivity for individual transcripts, detects needed to quantify specific genes of interest in tens of thousands nonpolyadenylated and low-abundance transcripts, and can profile of single cells in a cost-effective and sensitive manner. more than 100,000 single cells. We demonstrate how HyPR-seq can profile the effects of CRISPR perturbations in pooled screens, detect Significance time-resolved changes in gene expression via measurements of gene introns, and detect rare transcripts and quantify cell-type fre- quencies in tissue using low-abundance marker genes. By directing Gene expression is precisely controlled across human cell types sequencing power to genes of interest and sensitively quantifying to control cellular functions and phenotypes. Here we present individual transcripts, HyPR-seq reduces costs by up to 100-fold com- a technology to quantify the level of expression of genes in pared to whole-transcriptome single-cell RNA-sequencing, making single cells, which will help to understand how genes are HyPR-seq a powerful method for targeted RNA profiling in single regulated in each cell type in the human body. We demonstrate cells. that this technology improves upon existing tools to enable understanding how DNA regulatory elements control gene expression and mapping the frequencies of different cell types gene regulation | single cell | enhancers | genomics in tissues. This technology will enable new large-scale studies to understand gene control. ingle-cell RNA sequencing (scRNA-seq) has emerged as a Spowerful and flexible tool for characterizing biological sys- Author contributions: J.L.M., B.R.D., V.S., P.G., L.M.C., S.G.R., K.Z., C.P.F., A.G., E.S.L., F.C., tems (1–5). The ability to measure gene expression in thousands and J.M.E. designed research; J.L.M., B.R.D., V.S., P.G., L.M.C., S.G.R., K.Z., C.P.F., E.J.G., of single cells at once has enabled identifying and characterizing S.M., D.T.B., and F.C. performed research; J.L.M., B.R.D., V.S., P.G., Q.W., L.M.C., S.G.R., K.Z., C.P.F., J.N., T.N., F.C., and J.M.E. analyzed data; and J.L.M., B.R.D., F.C., and J.M.E. cellular heterogeneity in tissues, defining gene programs in previ- wrote the paper. ously uncharacterized cell types, and studying the dynamics of gene Reviewers: A.M., Max Planck Institute for Molecular Genetics; K.P., David Geffen School of expression (6). scRNA-seq has also facilitated high-throughput Medicine, University of California; and C.J.Y., University of California, San Francisco. genetic screens that link CRISPR perturbations to effects on Competing interest statement: J.L.M., V.S., S.G.R., F.C., and J.M.E. are inventors on patent transcriptional programs in single cells (7–9). applications filed by the Broad Institute related to this work (62/676,069 and 62/780,889). However, while scRNA-seq addresses certain biological ques- E.S.L. serves on the Board of Directors for Codiak BioSciences and Neon Therapeutics, and tions by capturing an unbiased representation of the transcriptome, serves on the Scientific Advisory Board of F-Prime Capital Partners and Third Rock Ven- tures; he is also affiliated with several nonprofit organizations, including serving on the the utility of this approach is limited for applications that require Board of Directors of the Innocence Project, Count Me In, and Biden Cancer Initiative, and sensitive detection of specific transcripts of interest. Such appli- the Board of Trustees for the Parker Institute for Cancer Immunotherapy. E.S.L. has served cations include profiling the dynamics of low-abundance or non- and continues to serve on various federal advisory committees. polyadenylated transcripts, quantifying rare cell types or states by Published under the PNAS license. measuring predefined marker genes, and characterizing the effects 1J.L.M. and B.R.D. contributed equally to this work. of perturbations to cis-regulatory elements on nearby genes. 2Present address: Petri Bio, Boston, MA 02111. Addressing these questions requires considering the efficiency of 3Present address: Bristol Myers Squibb, Cambridge, MA 02142. detection for individual RNA transcripts [which can range from 5 4E.S.L., F.C., and J.M.E. contributed equally to this work. to 45% for whole-transcriptome scRNA-seq sequenced to satu- 5To whom correspondence may be addressed. Email: [email protected], chenf@ ration (10–12)] and the depth of sequencing required to sample broadinstitute.org, or [email protected]. genes of interest (which might require hundreds of thousands of This article contains supporting information online at https://www.pnas.org/lookup/suppl/ reads per cell for lowly expressed transcripts). To address the doi:10.1073/pnas.2010738117/-/DCSupplemental. latter, previous studies have introduced several strategies to enrich First published December 21, 2020. 33404–33413 | PNAS | December 29, 2020 | vol. 117 | no. 52 www.pnas.org/cgi/doi/10.1073/pnas.2010738117 Downloaded at Harvard Library on April 17, 2021 We developed an approach called Hybridization of Probes to To do so, HyPR-seq involves hybridizing single-stranded DNA RNA sequencing (HyPR-seq) that enables targeted quantifica- (ssDNA) probes to one or more RNA transcripts of interest, tion of RNAs in thousands of single cells. Our approach builds encapsulating single cells in 1-nL droplets, PCR-amplifying the on the observation that single-molecule fluorescence in situ hy- ssDNA probes, and sequencing these amplicons to quantify gene bridization (smFISH)—involving hybridization of labeled probes abundance in each cell. This method has greater than 20% to target RNAs—can detect both polyadenylated and non- sensitivity for individual transcripts, can simultaneously measure polyadenylated RNAs with very high sensitivity (20–24). We hundreds of polyadenylated and nonpolyadenylated transcripts, sought to develop a method that quantifies the same hybridiza- and can profile more than 100,000 cells in a cost-effective tion probes via next-generation DNA sequencing, rather than via manner. We demonstrate the utility of this approach by mea- imaging, to enable simultaneous readouts of hundreds of probes suring the cis-regulatory effects of CRISPR perturbations to in thousands of single cells. noncoding DNA elements, detecting nonpolyadenylated intronic Design probes for HyPR-seqR-sR protocol GAPDH GATA1 AB1-100 chosen genes C Probe binding sites RNA 1 Fixed cells Apply HyPR RNA 2 probes HCR Cells with Hybridize HyPR probes initiator probes Primers with sample barcode Phosphate Initiator Beads with primers with bead barcode UMI P Generate droplet 5’ RNA emulsion 200 200 Add H1 H1 150 150 oil GENETICS hairpin 5’ 100 100 P PCR mix HyPR-seq 50 50 Number of cells 5’ Number of cells Release barcoded Add readout primers 0 0 oligo 5’ 5’ 100 P Ligated PCR template 5’ 5’ PCR in emulsion 20 75 Ligate Sample barcode UMI Probe Bead barcode 50 5’ 10 5’ 5’ 25 5’ 10x scRNA-seq 5’ Number of cells (x1,000) Sequence HyPR-seq library 0 Number of cells (x1,000) 0 PCR template: 500 1000 0 50 100 5’ Quantify RNA from probe UMIs UMIs/cell UMIs/cell D E F G H 3.0 200 100 Probe 1 TUBB R = 0.90 8 R = 0.98 BC1 (881) Probe 2 100 PGK1 2.5 BC2 (1070) 80 Probe 3 GATA1 6 150 Mixed (29) 2.0 60 10 4 100 1.5 40 fold-change) UMIs/cell 2 2 HyPR-seq avg.
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