bioRxiv preprint doi: https://doi.org/10.1101/2020.04.21.054296; this version posted April 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
Effect of storage conditions on peripheral leukocytes transcriptome
Yanru Xing1,2, Xi Yang2,5, Haixiao Chen2, Sujun Zhu3, Jinjin Xu2, Yuan Chen2, Juan Zeng3, Fang Chen2, Mark Richard Johnson4, Hui Jiang 1,6#, Wen-Jing Wang2#
1 School of Future Technology, University of Chinese Academy of Sciences, Beijing, China; 2 BGI-Shenzhen, Shenzhen 518083, China; 3 Obstetrics Department, Shenzhen Maternity and Child Healthcare Hospital, Shenzhen, Guangdong Province, China; 4 Academic Obstetric Department, Imperial College London, Chelsea & Westminster Hospital campus, London, UK 5 ShenZhen Engineering Laboratory for Innovative Molecular Diagnostic, BGI-Shenzhen, Shenzhen 518083, China. 6 Guangdong Enterprise Key Laboratory of Human Disease Genomics, Shenzhen Key Laboratory of Genomics, BGI-Shenzhen, Shenzhen 518083, China.
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Abstract
Introduction: Peripheral blood leukocytes are essential components of
the innate and adaptive immune responses. Their transcriptome reflects
an individual’s physiological and pathological state, consequently bulk
and single-cell RNA sequencing have been used to assess health and
disease. As RNA is dynamic and could be affected by ex vivo conditions
before RNA stabilization, we have assessed the influence of temporary
storage on the transcriptome.
Methods: We collected peripheral blood from six healthy donors and
processed it immediately or stored it either at 4 or room temperature
(RT, 18–22 ) for 2h, 6h and 24h. Total cellular RNA was extracted from
leukocytes after red blood cells lysis and the transcriptome analyzed
using RNA sequencing.
Results: We identified 152 up-regulated and 12 down-regulated coding
genes in samples stored at 4 for 24h with most of the up-regulated
genes related to nucleosome assembly. More coding genes changed
expression at RT, with 1218 increased and 1480 decreased. The increased
genes were particularly related to mRNA processing and apoptosis, while
the decreased genes were associated with neutrophil activation and
cytokine production, implying a reduced proportion of neutrophils, which
was confirmed by leukocyte subsets analysis. Most house-keeping genes
changed expression during storage, but genes such as TUBB and C1orf43
2 / 21 bioRxiv preprint doi: https://doi.org/10.1101/2020.04.21.054296; this version posted April 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
were relatively stable and could serve as reference genes.
Conclusion: Temporary storage conditions profoundly affect leukocyte
gene expression profiles and change leukocyte subset proportions. Blood
samples stored at 4 for 6h largely maintain their original transcriptome.
Keywords: leukocyte, storage condition, RNA-seq, transcriptome profile
3 / 21 bioRxiv preprint doi: https://doi.org/10.1101/2020.04.21.054296; this version posted April 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
Introduction
Leukocytes are important components of the peripheral immune system
and play an essential role in protecting the body from infection. With the
development of next-generation sequencing techniques, bulk RNA
sequencing (RNA-seq) and single-cell RNA-seq have become
increasingly popular ways to study the activity and function of leukocytes
[1]. Bulk RNA-seq has revealed marked changes in the leukocyte
transcriptome in different diseases [2-4] and single-cell RNA-seq has not
only identified new subtypes of leukocytes [5, 6] but also found unique
inflammatory gene expression profiles [7] in specific diseases. Thus, both
bulk and single-cell leukocyte RNA-seq have established themselves as
valuable tools in health research.
Although RNA is dynamic, it can be stabilized and stored at -80 in
TRIzol Reagent for a long time before analysis, providing a convenient
solution for large sample collections [8]. However, usually, leukocytes
can not be isolated from blood immediately before TRIzol treatment,
which leads to unpredictable changes in the transcriptome. Given the
inherent challenges of blood collection tubes, leukocytes are susceptible
to metabolic stress due to the lack of glucose and oxygen [9, 10]. As a
result, the expression of hundreds of genes may be inadvertently changed.
To obtain accurate leukocyte transcriptome, the first step should be to
avoid or mitigate ex vivo changes on RNA [11]. PAXgene Blood RNA
4 / 21 bioRxiv preprint doi: https://doi.org/10.1101/2020.04.21.054296; this version posted April 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
system can stabilize blood RNA immediately after taking blood [12], but
single cells and plasma cannot be separated for further analysis. Blood
collected in EDTA tube can be used as a liquid biopsy, including bulk and
single-cell analysis, but little is known about the influence of temporary
storage conditions on the leukocyte transcriptome. Understanding the
impact of storage conditions on the transcriptome is critical for
researchers to obtain meaningful and reproducible results. To optimize
leukocyte transcriptome assessment we subjected peripheral blood to
different short-term storage conditions, 4 or room temperature (RT) for
various periods, and assessed the influence on the leukocyte
transcriptome.
5 / 21 bioRxiv preprint doi: https://doi.org/10.1101/2020.04.21.054296; this version posted April 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
Materials and Methods
Sample collection
Peripheral blood samples were obtained from three healthy women and
three healthy men and were processed immediately or stored either at RT
or 4 for 2h, 6h and 24h (Fig. 1A). Leukocytes were subsequently
isolated for RNA-seq.
RNA isolation and sequencing
RNA from leukocytes was extracted by the TRIzol Reagent (Thermo
Fisher, 10296028) following standard procedures as previously described
[13]. RNA concentration and integrity were measured and calculated by
Agilent 2100 bioanalyzer (Agilent Technologies, G2939A) and
accompanying software. All RNA samples were with amounts greater
than 200ng and RIN values of greater than 6 underwent library
construction. Libraries were built using RNase H method [14] and
sequenced on BGISEQ-500RS (single-end 50bp).
Data preprocessing
Ribosomal ribonucleic acid (rRNA) was removed by SOAP2 (Version
2.21t) [15] and reads with low quality and adaptors was filtered by
SOAPnuke (Version 1.5.6) [16] from raw data based on default
parameters. Clean data were obtained in the FASTQ format. Kallisto [17]
was used to align all clean reads to transcriptome references and obtain
estimated reads counts and transcripts per million mapped reads (TPM)
6 / 21 bioRxiv preprint doi: https://doi.org/10.1101/2020.04.21.054296; this version posted April 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
results. Transcriptome references were the refMrna.fa.gz file from the
UCSC database with the removal of NR_RNA [18] and the
NONCODEv5_human.fa.gz file from the NONCODE database [19].
Differentially expressed gene (DEG) analysis
We used edgeR [20] and QL F-test to identify DEGs between samples
stored under different conditions. Genes with count per million (CPM)
greater than 1 in at least six samples were retained. Trimmed mean of
M-values (TMM) [21] between each pair of samples was performed to
minimize the log-fold changes between the samples for most genes.
Q-values are calculated using the Benjamini-Hochberg [22] procedure to
account for multiple testing. Genes performed Q-values ≤ 0.01 and fold
change ≥ 2 between two groups were considered as DEGs.
Weighted Gene Co-expression Network Analysis (WGCNA)
We merged all DEGs and retained all samples’ TPM data of these genes
for WGCNA [23]. First, we used the scale-free topology criterion to
choose the soft threshold β that R2>0.8 and the adjacency was
transformed into topological overlap matrix (TOM) using a power
β function: amn = |rmn| (rmn: Pearson correlation coefficient for gene m and
n). Second, the hierarchical clustering was used to cluster the topological
overlap distance calculated by the adjacency matrix based on TOM
dissimilarity measure with a minimum gene number is 50 and amn larger
than 0.95. A module eigengene distance threshold of 0.25 was used to
7 / 21 bioRxiv preprint doi: https://doi.org/10.1101/2020.04.21.054296; this version posted April 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
merge highly similar modules. Finally, we calculated the Pearson
correlation between module eigengene [24] and storage time.
Functional analysis
The biological process of coding genes in modules was performed by
Metascape [25]. Top 5 clusters with their representative enriched terms
(one per cluster) in each gene module were presented. Genes were
annotated based on information in the GeneCards [26] and the Ensembl
database [27]. The expression of house-keeping genes [28] under
different storage conditions was described.
Leukocyte subsets analysis and statistical analysis
The proportion of leukocyte subtypes was estimated by CIBERSORT [29]
according to coding gene TPM. We used paired two-sided t-test to
compare the differences between samples stored under different
conditions and processed immediately, and p < 0.05 was considered to be
significant. Data were presented as mean ± SD.
8 / 21 bioRxiv preprint doi: https://doi.org/10.1101/2020.04.21.054296; this version posted April 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
Results
Transcriptome influenced by storage conditions
RNA was successfully extracted from all samples, and the quality of RNA
didn’t show significant differences. About 100M clean reads were
obtained from each sample for further transcriptome analysis. The
correlation of gene expression with samples processed immediately
decreased with time and the expression profiles were more stable at 4
than at RT (Fig. 1B). More coding and long non-coding genes changed
expression at RT than at 4 , and the number of DEGs increased with
time (Fig. 1C, 1D). Heatmap also showed that the expression of DEGs
changed more at RT than 4 , this affected coding genes or non-coding
genes similarly (Fig 1D, 1F).
Storage time-associated co-expression modules
According to the expression of DEGs from 4 and RT, the WGCNA
successfully identified three (Fig. 2A) and four (Fig. 2B) gene integrative
modules respectively. Further, we found the green and brown modules
showed negative correlations with storage time, while the blue and black
modules showed positive correlations (Fig. 2C, 2D). Gene numbers in the
different modules were counted (Fig. 2E, 2F). Samples stored at 4 had
14 down-regulated and 152 up-regulated coding genes, while samples
stored at RT had 1499 genes down regulated and 1186 genes up regulated
(Fig. 2G, 2H).
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Biological process of coding genes in different modules
Coding genes for biological processes of nucleosome assembly and
response to cAMP were up-regulated at 4 and were also enriched to a
lesser extent at RT (Fig. 2I). Coding genes up-regulated at RT were
specifically associated with mRNA processing, negative regulation of
protein modification process, apoptotic signaling pathway and negative
regulation of cell cycle (Fig. 2H). While down-regulated coding genes at
RT were particularly enriched for leukocytes activation processes in the
immune response, such as neutrophil activation, complement
receptor-mediated signaling pathway, cytokine production, interleukin-1
beta production and cell activation involved in immune response (Fig.
2H).
The change of leukocyte subsets
The proportions of different leukocyte subsets varied in different storage
conditions (Fig. 3A). Most subsets had constant relative proportions when
samples stored at 4 for 24h. However, the proportions of neutrophil,
memory B cell and naïve CD4 T cell significantly decreased and CD8 T
cell, resting memory CD4 T cell, regulatory T cell, and resting mast cell
increased in samples stored at RT for 6h or 24h (Fig. 3B).
House-keeping genes expression during storage
The expression of C1orf43, TUBB, CHMP2A, SNRPD3, and RAB7A
were relative stable both at RT and 4 within 24h, while HSP90AA1,
10 / 21 bioRxiv preprint doi: https://doi.org/10.1101/2020.04.21.054296; this version posted April 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
PSMB4, VCP, and REEP5 were significantly changed in samples stored
for 24h both at 4 or RT. The expression of the rest of house-keeping
genes was relatively stable in samples stored at 4 (Fig. 4A). We
identified 17 differentially expressed genes between females and males
under different storage conditions. Most DEGs expression was more
consistent at 4 than at RT. The expression of such DEGs located in
Y-chromosome and ZFP57 was independent of the storage conditions,
while the expression of some genes was only significantly different
between females and males when the samples stored at RT for 24h, such
as IL1RN and IL-1B (Fig. 4B).
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Discussion
How the mode of transport of peripheral blood samples between the
clinic and the laboratory before RNA stabilization could affect bulk and
single-cell RNA-seq of leukocytes remains unclear. Artificial changes,
unrelated to the study, could materially affect the results [30, 31]. Thus,
we investigated the effects of storage conditions on the leukocyte
transcriptome by comparing peripheral blood samples stored at 4℃ or
RT with those processed immediately. The transcriptome changed more
dramatically at RT than 4 ℃ , showing more up-regulated and
down-regulated genes and variable leukocyte subsets proportions. Blood
stored at 4℃ and processed within 6h largely retains its original status,
suggesting that samples should be stored at 4°C during transport.
Low temperatures can reduce metabolism, and previous studies have
shown that RNA is relatively stable at 4°C [32]. From our data, it can be
seen that the expression of most genes is relatively stable in samples
stored at 4°C and that only 0.88% (166/18777) of coding genes and 1.66%
(1349/81226) of non-coding genes were significantly influenced. Among
the differentially expressed coding genes, 152 coding genes are
up-regulated, and these genes were mostly enriched for nucleosome
assembly. Previous studies reported that as the temperature increases,
nucleosome DNA denatured [33] and our data indicate that low
temperatures might repress transcription by altering the structure of
12 / 21 bioRxiv preprint doi: https://doi.org/10.1101/2020.04.21.054296; this version posted April 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
chromatin. It is suggested that in studies designed to investigate the
structure of nucleosomes and related functions, sample storage
temperature should be taken into account.
Storing at RT within 24h did not influence RNA integrity of leukocytes,
but significantly affected transcriptomic profiles, with the gene
expression of 14.50% (2723/18777) of the coding genes and 9.97%
(8100/81226) of the non-coding genes being significantly changed.
According to the biology process of up and down-regulated coding genes,
leukocytes stored at RT possibly underwent apoptosis and neutrophils
activation. Blood cells preserved at EDTA tubes are under glucose and
hypoxia deprivation that promotes cascades of biological alterations,
causing inflammation [34, 35] and triggering neutrophils apoptosis
[36-38]. As RT has a major impact on leukocyte transcriptome, the
influence of temperature should not be ignored when dealing with
samples stored at such a condition.
It was inferred that apoptosis happened when blood was stored at RT, so
we used CIBERSORT to explore the leukocyte subsets proportions of
each sample. We found that the relative proportion of neutrophils
significantly decreased in samples stored at RT for 24h, while the
leukocyte subsets composition was relatively stable at 4 within 24h. As
we know, neutrophils are the most abundant cell type in leukocytes [39,
40]. They have a short half-life (7–12 h in vivo) in peripheral blood, and
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are generally functionally quiescent under normal conditions [41]. The ex
vivo temporary storage at 4 probably delays neutrophil apoptosis.
Different cell types might have distinct fates under the same storage
condition, leading to biased subsets composition. Therefore, the influence
of storage conditions on identifying new cell types or evaluating cell type
compositions especially in single-cell sequencing should be taken into
consideration.
House-keeping genes are ideally expressed at relatively constant levels in
most situations and critically important to our ability to identify changes
in gene expression [28]. However, a previous study [42] showed that the
expression of house-keeping genes could be altered by storage conditions,
and that caution must be exercised when selecting genes for reference
purposes. According to our results, C1orf43, TUBB, CHMP2A, SNRPD3,
and RAB7A could be used as reference genes for leukocyte samples.
IL1RN is associated with many autoimmune diseases like rheumatoid
arthritis [43], which has a high incidence in women. Our study showed
that IL1RN was not differentially expressed between males and females
in the samples processed immediately, but its expression was greater in
females than males after 24h storage at RT, which may indicate sex
differences in the inflammatory response [44]. It is suggested that gene
expression is not only affected by ex vivo conditions but also influenced
by the genetic background, consequently long-term storage before RNA
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stabilization could markedly alter the transcriptomic profile.
Conclusion
Peripheral blood collected into EDTA tubes, stored at 4 and processed
within 6h largely maintain the original transcriptome. Temporary storage
of blood samples at different temperatures and for longer periods could
change the gene expression profile and leukocyte subset proportions.
C1orf43, TUBB, CHMP2A, SNRPD3, and RAB7A could serve as
reference genes under such conditions. It is critically important to
consider how sample processing and storage could influence bulk and
single-cell RNA-seq to optimize the results of transcriptomic analysis.
15 / 21 bioRxiv preprint doi: https://doi.org/10.1101/2020.04.21.054296; this version posted April 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
Acknowledgments We are grateful to all healthy volunteers who donating blood for this study. We also thank Dr Qing Zhou and Dr Zhongzhen Liu in our team for sharing their advice on this study.
Compliance with Ethical Standards Conflicts of interest The authors report no conflicts of interest.
Funding This project is supported by the National Key Research and Development Program of China (No.2018YFC1004900), the National Natural Science Foundation of China (No.81300075), the Science, Technology and Innovation Commission of Shenzhen Municipality under grant (No. JCYJ20170412152854656, JCYJ20180703093402288).
Informed Consent All volunteers signed informed consent, and the study protocol was approved by the BGI Institutional Review Board (NO. BGI-IRB 17166).
Availability of data Raw RNA-seq data have been submitted to the CNGB Nucleotide Sequence Archive (CNSA: https://db.cngb.org/cnsa; accession number CNP0000266). Data generated or analyzed during this study are included in this published article.
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bioRxiv preprint doi: https://doi.org/10.1101/2020.04.21.054296; this version posted April 24, 2020. The copyright holder for this preprint (which A was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Peripheral blood collection (n=6)
4℃ RT
0h 2h 6h 24h 2h 6h 24h
Leukocyte isolation and RNA extraction
RNA sequencing
coding gene non−coding gene B coding gene non-coding gene C 4℃ RT 4℃ RT 3518 ● 3000 ● 0.98
● 2000 up
0.95 1277 1155 1218 1000
310 0.92 152 222 214 35 0 1 20 22
0 0 1 2 10 12 14 0.89 71 71 203
R − squared 1000 967
0.86 1480 down
Number of DGEs 2000 0.83 3000
0.80 4000 2h 6h 24h 2h 6h 24h 4381 2h 6h 24h 2h 6h 24h
D coding gene E non-coding gene Cluster Dendrogram (4℃) Module−trait relationships A C E coding gene non-coding gene bioRxiv preprint doi: https://doi.org/10.1101/2020.04.21.054296; this version posted April 24, 2020.1 The copyright holder for this4℃ preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 1276 ME −0.85 green (1e−07) 0.5 1000 Height 0.78
0.7 0.8 0.9 1.0 ME 0 blue (6e−06) 500 0.6
−0.5 Gene number 152 ME 0.13 Dynamic Tree Cut 72 as.dist(dissTOM) grey (0.60) 14 0 1 hclust (*, "average") −1 0 Merged dynamic green blue grey Time (4℃) B D F Cluster Dendrogram (RT) Module−trait relationships coding gene non-coding gene
1 RT ME −0.95 4483 (2e−12) brown 4000 0.5 3307 ME 0.80
Height 3000 black (3e−06) 0 2000 ME 0.23 1499 0.2 0.4 0.6 0.8 1.0 1186
darkgreen (0.30) Gene number −0.5 1000 282 Dynamic Tree Cut 0.18 as.dist(dissTOM) ME 35 3 28 hclust (*, "average") 0 grey (0.40) −1 Merged dynamic brown black darkgreen grey Time (RT)
G 4℃ down RT down H 4℃ up RT up
coding gene 2 12 1487 57 1091 95 non-coding gene 24 48 4435 680 596 2711
log10 (p-value) 15 I 13.65 6.22 0.00 GO:0006334: nucleosome assembly
6.70 4.68 0.00 GO:0051591: response to cAMP
5.62 2.97 0.00 GO:0043435: response to corticotropin−releasing hormone 10
5.12 4.88 5.29 GO:0009612: response to mechanical stimulus
4.72 7.73 0.00 GO:0048511: rhythmic process 6
0.00 15.50 0.00 GO:0006397: mRNA processing
2.23 14.30 0.00 GO:0031400: negative regulation of protein modification process 2
2.81 13.49 0.00 GO:0097190: apoptotic signaling pathway 0
0.00 10.75 0.00 GO:0045786: negative regulation of cell cycle
3.03 10.36 0.00 GO:0080135: regulation of cellular response to stress
0.00 0.00 6.64 GO:0042119: neutrophil activation
0.00 0.00 5.40 GO:0002430: complement receptor mediated signaling pathway
0.00 6.50 5.09 GO:0001816: cytokine production
0.00 0.00 4.90 GO:0032611: interleukin−1 beta production
0.00 0.00 4.88 GO:0050663: cytokine secretion 4℃ up RT up RT down bioRxiv preprint doi: https://doi.org/10.1101/2020.04.21.054296; this version posted April 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
A B cells naive B cells memory Plasma cells CD8 CD4 naive CD4 memory resting CD4 memory activated T cells follicular helper T cells regulatory T cells gamma delta NK cells resting NK cells activated Monocytes Macrophages M0 Macrophages M1 Macrophages M2 Dendritic cells resting Dendritic cells activated Mast cells resting Mast cells activated Eosinophils Neutrophils
0h 4℃ RT 100.00% t
n 80.00% e c r e
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B 0h 2h 6h 24h
4℃ RT ** 60.00% t n e c r e
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e ** v i t a l e
R 20.00% ** ** ** ** ** ** 0.00% CD4 CD4 B cells T cells Mast cells B cells T cells Mast cells CD8 CD4 naive memory Neutrophils CD8 CD4 naive memory Neutrophils memory regulatory resting memory regulatory resting resting resting 0h 1.80% 13.75% 6.19% 2.15% 0.32% 0.31% 54.71% 1.80% 13.75% 6.19% 2.15% 0.32% 0.31% 54.71% 2h 0.96% 16.24% 5.73% 1.78% 0.06% 0.62% 54.24% 1.76% 15.98% 4.96% 1.88% 0.53% 1.36% 54.13% 6h 1.55% 16.54% 6.55% 1.53% 0.09% 0.68% 51.23% 1.51% 17.38% 2.79% 1.42% 0.71% 2.57% 51.67% 24h 1.20% 18.13% 5.03% 0.25% 0.53% 0.75% 50.48% 0.81% 25.88% 0.63% 7.01% 1.31% 0.23% 26.34% bioRxiv preprint B A
log2 (TMM+1) log2 (TMM+1) 12.5 13.0 13.5 14.0 14.5 10 10.0 10.5 11.0 11.5 10.0 10.5 11.0 11.5 5 was notcertifiedbypeerreview)istheauthor/funder.Allrightsreserved.Noreuseallowedwithoutpermission. doi: h2 h24h 6h 2h 0h ** h2 6h24h 2h 0h h2 6h24h 2h 0h h2 6h24h 2h 0h ● ● ● ● https://doi.org/10.1101/2020.04.21.054296 ** 4℃ 4℃ 4℃ 4℃ DDX3Y (chrY) ** ● ** C1orf43 PSMB2 RPLP0 h2 h24h 6h 2h 0h ** h2 6h24h 2h 0h h2 6h24h 2h 0h h2 6h24h 2h 0h ● ● ● ● ** RT * ● ● ● RT RT RT * * ** ** 0 2 4 6 8 10.8 11.2 11.6 12.0 11.0 11.5 12.0 12.5 10 11 12 9 h2 h24h 6h 2h 0h ** h2 h24h 6h 2h 0h ● h2 6h24h 2h 0h h2 6h24h 2h 0h ** ● ● 4℃ 4℃ ZFP57 (chr6) ** 4℃ 4℃ ** TFRC TUBB GPI h2 h24h 6h 2h 0h ; ** h2 h24h 6h 2h 0h this versionpostedApril24,2020. h2 6h24h 2h 0h h2 6h24h 2h 0h ● ● ● ** RT * ● RT ● ● ● RT RT ** * ** ** ● 10 11 12 13 14 15 8.5 9.0 9.5 12.0 12.5 13.0 10.0 10.5 11.0 11.5 h2 h24h 6h 2h 0h h2 h24h 6h 2h 0h ● h2 6h24h 2h 0h h2 6h24h 2h 0h ● ● ● 4℃ ● 4℃ ● 4℃ 4℃ IL1RN (chr2) * ● ● ● ● HSP90AA1 CHMP2A VPS29 h2 h24h 6h 2h 0h h2 h24h 6h 2h 0h h2 6h24h 2h 0h h2 6h24h 2h 0h ● ● ● ● The copyrightholderforthispreprint(which RT ● ● * RT ● ● RT RT * * ● ** 10 12 10.0 10.5 11.0 10.0 10.5 11.0 10.0 10.5 11.0 11.5 8 9.5 9.0 9.5 h2 h24h 6h 2h 0h h2 6h24h 2h 0h 6h24h 2h 0h h2 6h24h 2h 0h ● ● 4℃ 4℃ 4℃ 4℃ * IL1B (chr2) SNRPD3 PSMB4 GUSB h2 h24h 6h 2h 0h h2 6h24h 2h 0h 6h24h 2h 0h h2 6h24h 2h 0h ● ● * RT * * ● ● ● RT RT RT * * ● ● ** 13.0 13.5 14.0 14.5 15.0 11.0 11.5 12.0 12.5 13.0 12.0 12.5 13.0 13.5 h2 6h24h 2h 0h h2 6h24h 2h 0h h2 6h24h 2h 0h F M ● ● ● ● 4℃ 4℃ 4℃ * ● GAPDH RAB7A VCP h2 6h24h 2h 0h h2 6h24h 2h 0h h2 6h24h 2h 0h ● ● ● * * ● ● ● RT RT RT * ** ● 10.0 10.5 11.0 11.5 17.0 17.5 18.0 18.5 6 7 8 h2 h24h 6h 2h 0h h2 6h24h 2h 0h h2 6h24h 2h 0h ● ● 4℃ ● 4℃ 4℃ * ● ● ● EMC7 ● REEP5 ACTB ● h2 h24h 6h 2h 0h h2 6h24h 2h 0h h2 6h24h 2h 0h ● ● * * ● ● RT RT RT * * * ● bioRxiv preprint doi: https://doi.org/10.1101/2020.04.21.054296; this version posted April 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
Fig. 1: (A) Schematic of samples processing protocol: Whole blood was collected in EDTA tubes. Leukocyte isolated, RNA extracted and sequenced after stored in different conditions. (B) R-square between samples under different storage conditions and samples treated immediately. (C) Numbers of DEGs between samples under different storage conditions and samples treated immediately. (D, E) Heatmap represented different expressed coding and non-coding genes under different storage
conditions. All gene expressions were transformed to log2(TPM+1)-mean0h log2(TPM+1).
Fig. 2: (A, B) Gene dendrogram by DEGs expression of 4 and RT. The color row underneath the dendrogram showed the module assignment determined by the dynamic tree cut. (C, D) Heatmap of the correlation between module eigengenes and the storage time. Blue color represented a negative correlation, and red represented a positive correlation. (E, F) Coding and non-coding gene numbers in different modules. (G, H) Venn diagram showed the overlap of down/up-regulated genes between RT and 4 within 24h. (I) Comparison of the biology process of up-regulated genes at 4 , RT and down-regulated genes at RT. Top 5 clusters with their representative enriched terms (one per cluster) in each gene module were presented (Methods).
Fig. 3: Leukocyte subsets analysis. (A) The relative percent of different cell subsets in different storage conditions. (B) Comparison of the relative percent of B cells memory, CD8, CD4 naive, CD4 memory resting, T cells regulatory, mast cells resting and neutrophils between samples under different storage conditions and samples treated immediately. * means p-value <0.05, ** means p-value <0.01.
Fig. 4: Expression of certain genes under different storage conditions. (A) Expression of house-keeping genes under different storage conditions. * means p-value <0.01, ** means p-value <0.01& fold change > 1. (B) Expression of DDX3Y, ZFP57, IL1RN and IL1B under different storage conditions. These genes performed different expression in genders. ** means p-adjust < 0.01 & fold change > 1. Gene expressions was transformed to log2(TMM+1).