University of Groningen
On the molecular mechanisms of hematopoietic stem cell aging Lazare, Seka Simone
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CHAPTER 4
A comprehensive analysis of differentially
ex-pressed transcripts during hematopoietic stem
cell aging in the mouse
Seka Lazare
1, Albertina Ausema
1, Ellen Weersing
1, Erik Zwart
1, Diana
Spierings
1, Nancy Halsema
1, Vicor Guryev
1, Ronald van Os
1,2, Leonid
Bystrykh
*1, Gerald de Haan
*1,21 European Research Institute for the Biology of Ageing,
2 Mouse Clinic for Cancer and Aging, University Medical Center Groningen, University of Groningen Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands,
Abstract
Hematopoietic stem cells (HSCs) replenish all peripheral blood cell types throughout the lifespan of an organism. Although HSCs have extensive self-renewal potential, during aging these cells show marked functional impairment. As many hematological conditions are strongly age-dependent, it is plausible that functional decline of HSCs underlies these age-associated pathologies. To identify the molecular mechanism of HSC aging, prior studies have aimed to identify the aberrant expression of HSC ‘aging genes’. Individually, these studies have not provided a consensus HSC aging signature. Here we used an RNA-seq to characterize gene expression on an individual mouse basis. We report increased transcriptional activity and increased expression variability in aged HSCs. We overlaid our data set with 6 other genome-wide transcriptome studies to generate a robust collection of a surprisingly small number of transcripts that are up- or downregulated during HSC aging. The majority of the differentially expressed genes were involved in regulating gene expression or encoded for cell surface molecules. Our study provides a resource for identifying candidate genes consistently deregulated in aging, many of which have not been implicated in HSC function or aging before.
Introduction
Hematopoietic stem cells (HSCs) have been shown to have a lifespan exceeding that of their original donor. When serially transplanted from one recipient to another, murine HSCs are able to sustain blood cell production for up to 60 months - more than two-fold the lifespan of a C57BL/6 mouse 1–3. Yet, paradoxically, when left unperturbed within
the bone marrow of an aging individual, they show signs of aging that are manifested in their reduced function 4,5. In humans, functional and phenotypical characteristics
associated with HSC aging have been more difficult to study, however human aging is strongly associated with increased susceptibility to infection, supposedly as a result of a dampened immune response, an increased incidence of anaemia 6,7, and an enhanced
incidence of hematological malignancies, alluding to the dysfunction of primitive cells 8.
When young or aged murine HSCs are transplanted in young recipients, aged HSCs show substantially impaired functional activity compared to young HSC 4,9. If
young and aged HSCs are co-transplanted in a single recipient, the large majority of peripheral blood cells is derived from the young stem cells. This, as well as other similarly consistent and strain-dependent changes 10,11 strongly suggest that HSC aging
is at least in part intrinsically controlled, and that aged HSCs are not rejuvenated by heterochronic transplantion in young hosts.
Two prominent cell-intrinsic mechanisms that could contribute to HSC aging relate to the accumulation of genetic and epigenetic aberrations. Whereas genetic changes include the random accumulation of DNA mutations, structural variants, and telomere erosion, epigenetic abnormalities refer to the loss or gain of epigenetic DNA or histone moieties that specify HSCs when they are first born during development.
Many epigenetic marks have been shown to specifically enriched at loci important for stem cell functioning, and indeed many epigenetic writers, erasers and readers have shown to be important for proper HSC functioning 12–14. In addition, several studies
have reported age-dependent changes in gene methylation and gain and loss of histone marks 15,16.
Although our understanding of how these various age-dependent genetic and epigenetic variations individually contribute to the observed aging phenotypes is limited, it seems plausible that collectively they result in marked transcriptional changes. Those changes, however, can be seen as either predetermined age-related developmental program, or it is more a result of selective evolution of particular HSC clones better adapted to their own survival in a time.
Several studies have been performed where highly purified HSCs were isolated from young and aged donor mice, and genome-wide transcriptomes were generated 5,15,17–20.
Although the aim of all of these studies was to identify specific transcripts that were up- or downregulated during HSC aging, the experimental setup and methodological detail were quite distinct (Table 1). This includes the platform with which RNA abundance was quantified, the age of the mice, and the sorting strategy to isolate primitive cells. So far, no meta-analysis of the available gene expression data sets has been performed to confirm or refute the age-dependent aberrant expression of specific transcripts. In contrast, multiple papers have been reported in which single genes are studied that are supposedly activated or repressed during HSC aging 21–23, but expression of these genes
has not necessarily been confirmed to be aberrant by the existing data sets. Importantly, all published studies isolated HSCs from pools of bone marrow cells obtained from multiple donors (Table 1). As it is well documented that characteristics of aging and functional decline can vary greatly between individual mice as they age 9,24, pooling
of HSCs from individual old mice may obscure meaningful gene expression changes that occur in some mice, but not others.
We therefore decided to carry out re-evaluation of the age-related change in the mouse transcriptome based on our own data in which RNA was sequenced from highly purified HSCs from multiple individual young and old donor mice. Secondly, we performed a meta-analysis of all available HSC aging gene expression data sets, and overlaid this with our newly generated data. We showed that there is increased transcriptional activity in aged HSCs. Similar to the increased functional heterogeneity of aging phenotypes, we show that there is substantial heterogeneity in gene expression between individual mice. Notwithstanding such heterogeneity, our stringent analysis generated a comprehensive collection of differentially expressed transcripts during hematopoietic stem cell aging in the mouse. This, surprisingly small, collection of robustly differentially expressed genes will be highly useful to the field and provides many entries into functional studies to delineate the impact of gene expression changes to age-associated HSC impairment
Table 1: Study Details of studies that pr
eviously investigated gene expr
ession changes in HSCs as they age.
Study DOI Year Mouse Strain Sex Age Young Age Old Replicates Y/O HSC Markers Pooled mice per library Microarray/ RNA-seq Platform Rossi et al 10.1073/ pnas.0503280102 2005 C57BL/6 N/A 2-3 22-24 3/5 LSKflk2 -CD34 -35–40 (young), 5–10 (old) Microarray Af fymetrix
GeneChip Mouse Genome 430 2.0
Chambers et al 10.1371/journal. pbio.0050201 2007 C57Bl/6 CD45.1 Male 2, 6, 12 21 2/2 Hoechst SParKLS 2-5 mice Microarray Af fymetrix (MOE430A) Noda et al 10.1016/j. bbrc.2009.03.153 2009 C57BL/6 N/A 3 18-24 2/2 LSKCD34 -N/A Microarray Af fymetrix
GeneChip Mouse Genome 430 2.0
W ahlestedt et al 10.1 182/ blood-2012- 11-469080 2013 C57BL/6 Male 3-4 21-24 3/3 LSKCD48 -CD150 + N/A (20,000 cells) Microarray Af fymetrix
GeneChip Mouse Genome 430 2.0
Beerman et al 10.1016/j. stem.2013.01.017 2014 C57BL/6 Male 3.5 25 4/3 LSKCD34 − Flk2 − CD150 + N/A Microarray Af fymetrix
GeneChip Mouse Genome 430 2.0
Sun et al 10.1016/j. stem.2014.03.002 2014 C57BL/6 Male 4 24 2/2 Hoechst SParKLS CD150 + N/A (70,000 cells) RNA-seq Illumina HiSeq 2000 Our Study 2017 C57BL/6 Male 6 24 4/5 LSKCD48- CD150+ No RNA-seq Illumina HiSeq 2500 Studies dif
fered on number of mice, ages, replicates
and HSC markers used for isolation.
The analysis platform
also dif
fered between
some studies. Notably
, all
Methods
Mice
Male C57BL/6 mice, W41.SJL and C57BL/6.SJL used in this study were obtained from the Central Animal Facility (CDP) at the University Medical Center Groningen. Mice were aged in our Institutional facility under SPOF conditions. All experiments were approved by the appropriate regulatory authorities.
HSC Isolation and antibodies for flow cytometry
Bone marrow cells were isolated by crushing tibia, femura, pelvis, sternum and spine of individual mice. Cells isolated from distinct mice were never pooled. Red blood cells were lysed with erylysis buffer and subsequent bone marrow was stained with a cocktail of antibodies against lineage markers (B220 Alexa 700, CD3 Alexa 700, Gr-1 Alexa 700, Mac-1 Alexa 700 and Ter-119 Alexa 700), c-Kit Phycoerythrin, Sca-1 Pacific Blue, CD48 Alexa 647, CD150 PeCy7, CD34 FITC, EPCR-Biotin and Streptavidin APCy7 (Biolegend). LT-HSC cells were isolated on a Moflo Astrios or XDP cell sorter (Beckman Coulter).
Transplantation Assay
15 young and 30 old donor HSC (Lin-Sca+C-kit+CD48-CD150+CD34-EPCR+) from
C57BL/6 mice were transplanted into 2.5Gy irradiated W41.SJL recipients. Blood was collected for chimerism analysis every 4-6 weeks post-transplant. For blood chimerism analysis, 250ul of peripheral blood was lysed with erylysis buffer and stained with the following antibodies: CD45.2 Phycoerythrin, CD45.1 Pacific Blue, CD3 APC, B220 FITC, Gr-1 PeCy7 and Mac-1 PeCy7. Samples were acquired on BD FACS Canto and analysed using Kaluza software (Beckman Coulter).
Immunofluorescence Staining
4000-6000 LT-HSC (Lin-Sca+C-kit+CD48-CD150+) were seeded onto spots on an
adhesion immunofluorescent slide (VWR). For RNA staining, cells were fixed with 100% methanol for 10 minutes and washed with PBS. Cells were subsequently stained with 1:10,000 SYTO RNASelect Green Fluorescent Stain (ThermoFisher) for 20 minutes at room temperature. After washing, coverslips were mounted with ProLong Diamond Antifade Mountant with DAPI.
For RNA-Polymerase II staining, cells were fixed and permeablized with Fixation/ Permeablization Solution Buffer (BD Bioscience) for 20 minutes on ice. After washing, cells were blocked first with Endogenous Biotin-Blocking Kit (ThermoFisher) followed by 4% BSA for 30 minutes. Cells were stained with 1:100 biotin mouse monoclonal RNA Polymerase II antibody (Novus Bio) at 4°C overnight. Cells were then washed with 0.1% Triton-X-100 and stained with 1:1000 streptavidin Alexa-488 secondary antibody. After washing, coverslips were mounted with ProLong Diamond Antifade Mountant with DAPI.
Image J 25.
Intracellular Flow Cytometry
10,000 LT-HSC (Lin-Sca+C-kit+CD48-CD150+) were fixed with 100% methanol for
10 minutes and washed with PBS. Cells were subsequently stained with 1:10,000 SYTO RNASelect Green Fluorescent Stain (ThermoFisher) for 20 minutes at room temperature. After washing, cells were acquired on BD FACSCanto II flow cytometer, and data analyzed using Flowjo (FlowJo LLC).
Low Input RNA-seq
RNA was isolated from 15,000 HSC (Lin-Sca+C-kit+CD48-CD150+) using the
Nucleospin XS kit (Macherey Nagel) and quantified on Bionalyzer using RNA Pico 6000 Kit (Agilent). Ribosomal depletion was performed using a modified version of RiboZero Kit (Illumina). 300pg ribosomal-depleted RNA was used as input into TotalScript RNA-Seq Kit (Epicentre). Samples were quantified and quality controlled using Qubit High Sensitivity Kit (Life Technologies) and DNA High Sensitivity Kit (Agilent). Libraries were pooled and sequenced to 30-50 million reads on HiSeq 2500.
Data Analysis
Pre-processing and Mapping
FASTQ Files were pre-processed to remove adapter sequences and low-quality sequences using Fast-X-Toolkit (http://hannonlab.cshl.edu/fastx_toolkit) and Trimmomatic (Bolger, 2014). Filtered FastQ files were then mapped to the UCSC mouse reference genome mm10 (iGenomes) using TopHat (http://ccb.jhu.edu/ software/tophat/index.shtml) (release 2.01.13). Library quality was confirmed using Picard CollectRNASeqMetrics (http://broadinstitute.github.io/picard/) and only libraries with over 80% correct strand reads and less than 1% ribosomal reads were used for differential expression analysis. Libraries from 4 young and 5 old mice passed this threshold.
Differential Expression Analysis
The Python Package HTSeq was used to count raw reads per gene using a version of UCSC mm10 gene annotation file modified to lack ribosomal RNA (rRNA) and transfer RNA (tRNA) to minimise technical variation from inefficient depletion of rRNA in sample preparation and variation from abundant tRNA which rarely approach saturation at the sequencing depths used (http://www-huber.embl.de/HTSeq/).
Differential expression analysis was performed in edgeR 26,27 (package version 3.16.5).
Genes with less than 10 reads in at least 3 samples were excluded from the analysis. Raw read counts were normalized using upper quartile normalization. Gene variance was modelled using tagwise dispersion estimates and differential expression testing was performed using the generalized linear model likelihood ratio test. Statistically significant differentially expressed genes were taken as p value less than 0.05 and Benjamini-Hochberg false discovery rate (FDR) of less than 5%. A heatmap of the top
100 differentially expressed genes was plotted using the heatmap.2 function from R package Gplots.
Determination of Differentially Expressed Genes in Individual Old Mice
Genes were taken to be differentially expressed in an individual old mouse if the normalized read count (upper quartile normalization performed in edgeR) for that gene was more than 1.5 times outside the interquartile range of its normalized expression of young mice. That is, if Q1(y)- 1.5×IQR((y)>x or x>Q3(y)+1.5×IQR(y) where y is the normalized expression for all young mice, and x is the normalized expression of a gene for a single old mouse, then that gene was called as differentially expressed for an individual old mouse.
Results
HSCs numbers and function alter during aging.
To search for genes that were differentially expressed during HSC aging, we sequenced ribosome-depleted RNA isolated from LT-HSCs from 4 individual young and 5 individual old female C57BL/6 mice. As has been shown before 4,5, the frequency of
Lin-Sca1+c-kit+CD48-CD150+ LT-HSCs increases strongly with age (Figure 1A, 1C).
As the overall cellularity of the bone marrow also increases with age (Figure 1B), the absolute number of Lin-Sca1+c-kit+CD48-CD150+ LT-HSCs increases gradually,
but ultimately very dramatically between 6 and 24 months (Figure 1D). To confirm functional activity of purified Lin-Sca1+c-kit+CD48-CD150+ LT-HSCs, 15 (young)
or 30 (aged) cells were transplanted to sub-lethally irradiated W41.SJL recipients, without any competitor cells. Whereas chimerism levels in recipients transplanted with 15 young HSCs reached on average 60%, chimerism levels in mice transplanted with 2-fold more aged HSCs were much lower (Figure 1E). These data confirm functional activity of the purified cells from which the RNA libraries were prepared, and document the strong age-dependent loss in potency. In addition, and as expected, we observed very substantial age-dependent mouse-to-mouse variability in the frequency of LT-HSCs (Figure 1C); whereas all 6-month-old mice show a very defined HSC pool size, upon aging deregulation of HSC pool size becomes apparent.
Aged HSCs display increased transcriptional activity.
Beyond relative gene expression differences in young and old HSCs, we also their assessed genome-wide transcriptional activity. Remarkably, the RNA yield isolated from HSCs from 24-month-old mice was ~3-fold higher than isolated from the same number of 6-month-old mice (Figure 2A). Moreover, RNA yield per cell was highly correlated to the size of the HSC pool in the mouse from which the HSCs were isolated (Figure 2B). We used two approaches to validate the increase in RNA content of aged HSCs at the single cell level. First, HSCs were stained with an RNA-specific dye, after which fluorescence was measured using confocal microscopy (Figure 2C and 2D) or by flow-cytometry (Figure 2E). In all analyses, on average aged HSCs contained ~3 fold more RNA than their young counterparts. As a second approach to quantify transcriptional activity in aged and young HSCs, we stained purified cells
LSK 1.12 100 102 104 Sca-1 100 101 102 103 104 105 C-Kit LSK 0.64 100 102 104 Sca-1 100 101 102 103 104 105 C-Kit LSKCD48+ CD150+ 2.11 LSKCD48+ CD150-7.70 LSKCD48-CD150+ 76.7 LSKCD48- CD150-13.5 100 102 104 CD150 100 101 102 103 104 105 CD48 LSKCD48+ CD150-46.6 LSKCD48+ CD150+ 2.98 LSKCD48-CD150+ 11.4 LSKCD48-CD150-39.0 100 102 104 CD150 100 101 102 103 104 105 CD48 B C D E 24 mon ths A 6 mon ths 6 12 18 24 0 20 40 60 80 100 Age (months) BM C/ HL x 1 0 6 *** **** ** ** 6 12 18 24 0.0 0.1 0.2 0.3 0.4 Age (months) H SC F re qu en cy **** * * ** **** **** 6 12 18 24 0 5×1004 1×1005 2×1005 2×1005 Age (months) H S C /H L **** ** * **** **** **** 6 24 0 20 40 60 80 100
Age of HSC donor (months)
% B lo od C hi m er ism *
Figure 1 Phenotypic Changes in the HSC Compartment with age. A:
Flow cytometry data showing increase in HSCs (Sca+C-kit+CD48-CD150+) with age. Aged (24-month-old) HSC show an increase in Lin-Sca+C-kit+ subset and the proportion of these cells which are CD150+ is drastically increased with age. B-C: Bone marrow cellularity normalized per hind-limb and HSC Frequency as measured by flow cytometry for mice at 6 to 24 months. Both bone marrow cellularity and HSC Frequency increase significantly with age and variation for both parameters and variation is also increased as measured by F-test D: The absolute number of HSCs also increases with age. HSC number/hind limb is calculated as a product of bone marrow cellularity and HSC Frequency. E: Week 32 blood chimerism of mice transplanted with 15 young or 30 old HSC.
Aged HSCs display reduced repopulating ability compared to young. (*
denotes significant T-test, p-value <0.05) (* denotes significant F-test p-value <0.05).
A B
C
F DAPI POL2 MERGE
O
ld L
T
Young L
T
DAPI RNA MERGE
E RNA Fluorescence Young HSC Old HSC ***** D D G Young HSC Old HSC 0 2×1004 4×1004 6×1004 8×1004 Po ly m er as e II **** Young HSC Old HSC 0 5.0×1003 1.0×1004 1.5×1004 2.0×1004 2.5×1004 R N A F lu or es ce nc e (F IT C ) 6 months 24 months 0.0 0.1 0.2 0.3 0.4 0.5 Age of HSC donor RN A pe r c el l ( pg ) **** 0 5×1004 1×1005 2×1005 2×1005 0.0 0.1 0.2 0.3 0.4 0.5 LSK SLAM/HL R N A p er c el l ( p g ) R2=0.8047
Figure 2 Transcriptional Activity of aged HSCs increases with age. A: RNA
content per cell increased 3-fold with age at 24 months, compared to 6 months in HSCs. B: RNA content is positively correlated with HSC pool size (normalized per hind-limb). Solid line dictates line of best fit, dotted lines dictate 95% confidence bands. C: Confocal images of RNA content in young and old HSCs. Scale bar = 5µm D: RNA fluorescence per cell in young and Old HSCs. * denotes significant T-test p value (p<0.05) E: Histogram if RNA fluorescence in young and old HSCs as measured by flow cytometry. F: Confocal images of Polymerase II content in young and old HSC. Scale bar = 5µm. G: Polymerase II fluorescence per cell in young and old HSC. * denotes significant T-test p value (p<0.05)
with an antibody detecting RNA Polymerase II. Once more, RNA Pol-II levels were on average ~3-fold higher in aged cells.
RNA-Seq analysis reveals transcriptional heterogenity in aged LT-HSCs.
To assess genome-wide transcriptional consequences of aging, we sequenced 3ng of total (ribosomal RNA-depleted) RNA isolated from 15,000 Lin-Sca1+c-kit+CD48
-CD150+ LT-HSCs, derived from 4 young or 5 aged mice (Figure 3). Unsupervised
clustering clearly revealed distinct age-dependent gene expression profiles, but overall these gene expression data showed relatively modest changes with age, and the very large majority of HSC transcripts were not affected. We identified 129 transcripts that were significantly upregulated in aged mice compared to young mice, and reversely we found 49 transcripts to be significantly downregulated with age (Figure 3).
The fact that we analyzed gene expression profiles in individual mice allowed us to assess to what extent the overall differences in expression were consistent, or rather seen in some mice but not in others (Figure 4). Indeed, significant transcriptional heterogeneity was revealed within the 5 aged replicate samples. Most of the differentially expressed genes encoded for cell surface receptors, proteins involved in gene transcription, epigenetic regulators and histone-associated proteins, and several cell cycle regulators (Figure 4).
In an attempt to generate a definitive list of age-dependently expressed HSC transcripts, we carried out a meta-analysis in which our own RNA-Seq data were added to 6 other previously published datasets in which genome-wide transcriptomes of aged HSCs were generated (Table 1). Since these 6 previously published datasets were all generated on different platforms, using different technology and different algorithms, their re-analysis from raw data would be highly problematic. Therefore, we took a simple and straightforward approach by collecting from each of the 6 studies gene lists as they have been reported by the authors. This approach reveals the most reproducible and robust list of stem cell aging candidate genes, as it is independent of methodological, technical, and bioinformatic constraints.
The number of genes that was reported to be differentially expressed in aged HSC in these studies varied widely. Whereas Noda et al 6,7 reported the identification of 123
genes to be upregulated and 32 genes to be downregulated, Chambers 5 and Sun 15
found more than 1200 transcripts to up- and downregulated. Comparison of all the available datasets, including our own, revealed that the large majority (80-85%) of differentially expressed HSC transcripts were reported by only a single study, and were not found in others (Figure 5A and 5B). Indeed, there was not a single transcript that was found to be differentially expressed in aged HSC in all 7 studies, including our own. Nevertheless, the expression of a sizeable number of transcripts was reported to be affected by age in more than one study, and dozens of transcripts were reported by 3 or more studies.
Our own analysis allowed a comparison of variability of gene expression between individual mice, in contrast to the 6 previously published studies in which HSCs from multiple aged mice were pooled prior to analysis, Therefore, we were able to assess to what extent genes that were reported to be differentially expressed by only a single, 2, 3, 4, 5, or all 6 studies, showed variation in expression levels between individual mice (Figure 5C). Indeed, transcripts that were reported to be differentially expressed in 5 or 6 independent studies showed less expression variability between individual mice in our study, and consequently were detected by us in all 5 animals. Moreover, genes that were found in only one or two studies, showed significantly more transcriptional variation and mouse-to-mouse differences, but some of those could be found
age-Young1 Young5 Young3 Young4 Old6 Old4 Old2 Old1 Old3 Txnip Eltd1 Tmpo Top2a Mcm6 Selp Klhl4 Fut8 Tgm2 Itga6 Myof Dennd4a Sdpr Ly6e Stat3 Lamp2 Vwf Plcg2 Cpt1a Lgals3bp Cd34 Hist1h1b Ccna2 Pf4 Cdca3 Mir5114 Ckap2 Kntc1 Adamts6 Kif4 Flt3 Cetn3 Lipt1 Zg16 Stom Sbspon Tc2n Tm4sf1 Trpc1 Gstm2 Slco2a1 Cd38 Ptprk Rbpms2 Clip3 Gm7694 Rdh10 Ramp2 Osmr Jam2 Gem Cd53 Chrm3 Cldn5 Bcl3 Pbx3 Kif20b Dtl Ckap2l Ehd3 Dnm3 BC005764 Plscr2 Hk2 Itgb3 Sult1a1 Mt1 Nupr1 Prtn3 Eps8 Exoc6b Tbc1d8 Cpne8 Amotl2 Arhgap29 Grina Fyb Prcp Trim47 Dock9 Mpp6 Wbp2 Ezh1 Plk2 Vldlr Ptger4 Alcam Il2rg Pde3b Ptplad2 Cyp26b1 Cysltr2 Clu Clca1 Gda Enpp5 Fam63a Pam Neo1 Oxr1 −2 0 1 2 Row Z−Score Color Key
Young 1 Young 4 Young 2 Young 3
O
ld 5 Old 4 Old 2 Old 1 Old 3
4 Young 5 Old
15,000 HSCS 3ng Total RNA Low Input total RNA-seq Differential Expression
Identification of Consistent DE Genes in individual Old
A
B
Figure 3 Analyzing Gene Expression changes with age.
A: Experimental Layout. 15,000 HSCs per mouse were isolated from individual mice and 3ng RNA was used to generate low input RNA-seq libraries. Libraries were sequenced and gene expression data generated for each mouse. Gene expression data from 4 young and 5 old mice were used in differential expression testing for gene expression changes with age. B. Heatmap of the resulting differentially expressed genes.
related in some individual mice. Therefore, we might speculate, that at least some discrepancies in the lists of age-related differential expressions are due to the natural variability of age-related gene expression. Therefore, identifying the most consistently deregulated genes in individual mice will give meaningful insight into the molecular mechanisms that underlie HSC aging.
A comprensive list of differentially expressed HSC aging transcripts.
To establish the most comprehensive collection of murine LT-HSC aging transcripts, we shortlisted all genes that were consistently up- or downregulated in at least 4 independent studies (Figure 6). Interestingly, we found more genes to be upregulated during aging compared to genes that were downregulated. Stringent filtering resulted in the identification of 76 transcripts that are up with aging, and 19 transcripts that are down upon HSC aging. None of the genes were found to be differentially expressed in all 7 studies. There were 4 genes that were found to be upregulated in 6 studies, Alcam (Activated Leukocyte Cell Adhesion Molecule), Sdpr (Serum-deprived response gene, aka Cavin2), Socs3 (Suppressor of cytokine signalling 3, and Neo1 (Neogenin-1). Functional annotation revealed that a significant fraction of the genes listed in Figure 4 and 5 are involved in the regulation of gene expression, and also a sizeable number of cell surface molecules involved in adhesion and/or signalling was affected by aging. The aging HSC signature list contains multiple transcripts that encode for proteins that have previously been shown to important for hematopoiesis (Socs3, Socs2, Stat3, Hmga2, Dnmt3b, Cd34, Cd38, Mpo) but the role for the majority of the genes in the regulation of blood cell production remains unknown.
Discussion
In this manuscript we report the most robust list of differentially expressed genes during mouse hematopoietic stem cell aging to date. We show how in aged HSC global transcriptional activity is increased. Although aging is evidently associated with cell-to-cell, and mouse-to-mouse variation, aged HSCs are uniformly functionally inferior to their young counterparts. We propose that the list of differentially expressed transcripts that we present in the current paper contains the core set of genes that contribute to the functional demise of HSC during aging. Although it remains unclear at this point to what extent these HSC aging genes act independently, or rather form regulatory networks and operate in concert, multiple relevant biological patterns do emerge from our analysis.
An aspect emphasized in this paper is the increase in individual variation with age in HSCs. When comparing previous studies to our own data, we were surprised to note that the majority of genes reported to be deregulated with age were observed only in a single study. Thus, it was unsurprising that each study came to different conclusions as to the important underlying factors in HSC aging. All of these studies used pooled mice to study gene expression. As we have shown in this study, aging is associated with increased mouse-to-mouse variation in all associated phenotypic and molecular characteristics, underscoring the relevance of investigating HSC aging at
Figure 4 Categorizing Differentially Expressed Genes. Statistically significant differentially
expressed genes ordered by consistency in individual old mice. Solid squares represent genes significantly expressed in each old mouse (denoted by M1-5). Red dots represent upregulated genes, and blue dots downregulated genes. Gene functional categories are illustrated according to the key on the lower right-hand side.
an individual level.
We provide strong evidence for increased global transcriptional activity in aged HSCs. Given that we show that young HSCs are significantly lower in RNA content than aged HSCs, this is an important factor to consider when comparing the degree of and the number of genes differentially expressed during HSC aging. Our observations of increased RNA content and Pol II levels in aged HSCs coincide with our gene expression data, showing downregulation of histones and perturbed expression of many transcription factors. Indeed, the majority of differentially expressed transcripts was upregulated and not downregulated as a function of age. Downregulation of histones, histone linkers and other DNA packaging genes are predicted to lead to loss of transcriptional control. Indeed, loss of heterochromatin appears to be a universal occurrence in aging in many organisms 28 and this has been previously associated with
transcriptional deregulation and genome instability. Simultaneous detection of changes in histone marks and nucleosome mapping may provide a more detailed picture of changes in gene expression regulation. In addition, the changes in transcriptional activity of aged HSCs warrant a more sophisticated study of transcriptional dynamics e.g. transcription rate analysis.
We found multiple histone genes to be downregulated with age. Core histones bind to form the nucleosome while histone linkers bind the DNA in between these nucleosomes helping to form the higher order structure of chromatin. Regulators of histone acetylation, Ezh1, Dr1 and Nupr1 were upregulated with age. Ezh1, a component of the Polycomb complex, is able to methylate lysine 27 on histone H3 thereby maintaining transcriptional repression. Our lab has previously shown the importance of Polycomb complexes and their interaction partner Ezh2 as being important for hematopoietic development and maintaining the balance between self-renewal and differentiation 12,29. Dr1 regulates histone acetylation on H3 and H4 via its
involvement in the ATAC complex, while Nupr1 is a negative regulator of histone H4 ‘Lys-16’ acetylation (H4K16ac) through its inhibition of MSL1 activity. The increased transcriptional activity of aged HSCs that we report here may well be caused by loss of epigenetic memory, which in turn contributes to increased transcriptional noise. A large proportion of downregulated genes was associated with cell cycle progression and DNA repair (Tmpo, Rnaseh2b and Rfc2), and facilitate replication of DNA during the S phase. Other genes (Cdc7, Cdca3, Cdcna2, Pole, Dtl1) are involved in the transition through various phases of the cell cycle. These findings highlight the relevance for HSCs to maintain their quiescent state during normal steady state blood cell production.
Transcription factors and transcriptional activators were largely upregulated with age. Some of these genes are activated as a response to well-known signalling pathways while others act to activate these pathways. Transcriptional activators such as Rps6ka3, Tsc22d1 and Stat3 are activated in response to ERK/MAPK pathway, TGF-β and
1 1 2 2 3 15 1 1 3 3 1 5 6 1 2 1 12 1 1 1 13 1 11 217 3 2 1 14 3 1 1 3 3 92913 3 7 8 17 1027 79 39 7 14 4 518642 79 2447 255 895 1074 0 300 600 900 1200 Number of Genes ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Chambers200 Sun2014 Rossi2005 Wahlestedt2013 OwnData Beerman2013Noda2009 0 500 1000
Number Upregulated Genes
0 1000 2000 3000 &XP Number of Genes 111 121 117 2112 162 291 122 7941 121 251 1124 16 12 19 53 432575 6 20 43 177 232 1097 1006 0 250 500 750 1000 1250 Number of Genes ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Sun 2014 Chambers007 Beerman2014 Rossi2005 Wahlestedt2013 OwnData Noda2009 0 500 1000
Number Downregulated Genes
0 1000 2000 3000 &XP Number of Genes A B C
Figure 5 Multi-study analysis of reported differentially expressed genes. A. and B: Overlap
upregulated and downregulated genes, respectively, in our data and published studies. Horizontal bars represent number of genes reported by each study named to the right. Solid blue dots indicate overlap of genes between studies. The number of each gene in each overlap set is illustrated by vertical bars and denoted by numbers on top of each bar. Dotted lines represent cumulative number of genes starting from most overlap to least. C. Comparison of consistency of expression and differential expression of genes in our dataset compared to consistency in frequency of reporting in published studies. Solid dots in each row indicate genes categorized by the frequency they have been reported in our data and those previously published. The middle box plots represent deviation of expression of each category of genes in our data and the right boxplots represent the number of individual old mice these genes are differentially expressed in (as shown in Figure 4).
Figure 6 A comprehensive list of differentially expressed transcripts during hematopoietic stem cell aging.
interleukins, KITLG/SCF respectively 30–33. Rps6ka3 itself is able to activate mTor
signalling, and Blc3 regulates transcriptional activation of NFκ-B genes 34–36. Pbx3 is
associated with enhanced self-renewal and leukemic transformation 8.
Interestingly, a large number (~30%) of differentially expressed genes encode for cell-surface receptors or for proteins that act at the transmembrane domain of the cell, the large majority of which are upregulated with aging. Some of these are associated with HSC-lineage determination. Flt3, which we did not use in our sorting criteria for purification of LT-HSC, was downregulated in aged HSCs. Flt3 is widely known as an important regulator of hematopoiesis regulating differentiation, proliferation and survival of hematopoietic progenitor cells. Mutations resulting in a constitutively active form of the receptor commonly results in acute myeloid leukemia (AML) 4,9.
Low levels of Flt3 expression are thought to mark the most primitive HSC populations and knock-in of its constitutively active form leads to HSC exhaustion 41–43.
Cell surface receptors that were upregulated also play a role in cell-adhesion. Itgb3, a member of the integrin family of cell adhesion molecules may mediate cell-surface signaling 44. The cell adhesion molecule Alcam binds to CD6 and may play a role in
binding of immune cells to activated leukocytes 45,46. Lama5 and Neogenin mediate
the attachment, migration and organization of cells into tissues during embryonic development 47–49. The differential expression cell surface molecules offers the
opportunity to flow-cytometrically isolate HSC, which show higher expression of candidate receptors, and functionally test their stem cell potential. Presumably, some of the cell surface molecules could be used as markers to specifically identify aged HSCs. The upregulation of cell surface molecules during HSC aging also suggests that aged cells differentially interact with their immediate environment, either through altered adhesion properties or through changes in the kinetics with which aged HSCs respond the secreted ligands.
Presently theories of aging can be largely categorized into pre-programmed decline of function of aged cells or stochastic acquisition of variations resulting in clonal evolution. Our data showing decline in gene packaging and epigenetic modulators as a large proportion of gene expression changes in aging, as well as upregulation in RNA in old HSCs, may lend evidence towards the latter theory. Whereas the young HSCs show reasonably consistent pattern of phenotypical and genotypical features, old HSCs show considerable diversity. From this prospective, not surprisingly, the aging phenotype at the level of gene expressions is also quite elusive.
Although at this point the functional role of many of the core aging genes in HSC biology remains to be determined, our study did result in the identification of many individual HSC aging genes of which the general function is well understood, but which have not been associated with HSC aging. In conclusion, our study provides a valuable resource for identifying candidate genes consistently deregulated in HSC aging and opens up avenues for functional follow-up studies.
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Acknowledgements
This study was supported by Marie Curie Initial Training Networks grant “Marriage” funded by the European Union (Brussels, Belgium), the Mouse Clinic for Cancer and Ageing funded by a grant from the Netherlands Organization of Scientific Research (NWO), and a Systems Biology of Ageing Grant funded by NWO (The Hague, Netherlands) grant 853.00.110..
The authors thank W Abdulahad, T Bijma, G. Mesander, H Moes, and R.-J. van der Lei for cell sorting assistance and Klaas Sjollema from the UMCG Microscopy and Imaging Center (UMIC) for assitance with confocal microscopy.
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Receptors Transcription Factors Epigenetic Regulators Cell Cycle Development Ccnb2 Ccna2 Cdc7 Mcm6 Pole Mki67 Top2a Kntc1 Rfc2 Kif4 Ncapd2 Ckap2l Ckap2 Nusap1 Ckap5 Cdca3 Kif20b Dtl Cit Hist1h1b Tmpo Rnaseh2b Hist1h2bg Hist1h2ak Hist1h2bn Flt3 Ldha Pkm Aldh1a1 Rdh10 Stat3 Bcl3 Itga6 Lama5 Gata2 Vwf Lgals3bp Uba7 Sbno2 Hk2 Osmr Psmd7 Fyb Cd53 Clu Apoe Samsn1 Cldn5 Plcg2 Eltd1 Il2rg Phf11d Eps8 Mpp6 Glul Oat Ramp2 Wbp2 Grina Sult1a1 Ly6e Ezh1 Nupr1 Dr1 Rps6ka3 Tsc22d1 Pbx3 Tbxa2r Dock9 Itgb3 Neo1 Alcam Histones
Supplementary Figure 1 Interaction and Functional Categorization of Differentially Expressed Genes. Each octagon represents a differentially expressed gene (specified by the
name in the middle). Bold borders illustrate genes which have been implicated in Hematopoiesis through published experimental data. Genes reported to be interacting (by String-DB database) are connected by solid lines. Gene functions are illustrated by color according to key at top left and bottom right. Half bordered by red upwards arrow shows upregulated genes, and downwards arrow downregulated genes.
Supplementary Table 1 Differentially Expressed Genes
Gene logFC logCPM LR PValue FDR Young1 Young2 Young3 Young 4 Old1 Old2 Old3 Old4 Old5
Selp 2.599292737 8.2832211 217.7267 2.83E-49 3.69E-45 54.00794 102.8839 111.8097 85.08753 505.6912 599.9798 532.928 511.7414 340.9323 Klhl4 1.751946456 8.333439382 113.9281 1.35E-26 8.81E-23 104.3335 163.6139 171.641 136.4827 510.0962 413.7631 507.0218 468.6131 416.392 Enpp5 1.63211352 7.621656118 94.3377 2.66E-22 1.16E-18 113.5394 71.08989 100.9653 80.51907 285.4424 241.4779 304.7065 288.8512 280.9282 Gda 1.818393994 7.459447286 85.63162 2.17E-20 7.07E-17 58.30403 86.80825 55.34394 99.36396 236.9877 259.1244 307.1738 228.3266 228.1974 Plk2 1.755056152 7.215540589 81.98142 1.37E-19 3.58E-16 58.30403 68.94647 57.21367 74.23744 232.5827 224.2958 144.9515 220.3532 254.5628 Ramp2 3.299370255 5.62562253 73.87352 8.33E-18 1.81E-14 7.364719 11.0743 2.991565 10.85009 65.19364 53.86815 101.7745 103.653 67.27731 Sult1a1 1.711454713 6.786859521 71.29479 3.08E-17 5.73E-14 47.25695 52.51363 50.8566 47.96881 185.89 157.425 146.8019 186.6479 109.0983 Cd34 -1.229799515 7.654508879 69.01912 9.75E-17 1.59E-13 290.2927 310.795 326.8284 259.831 103.9574 131.4197 144.3347 123.5863 105.4617 Vldlr 1.882943997 6.899383309 67.86806 1.75E-16 2.53E-13 48.4844 52.87087 57.21367 35.9766 122.4583 210.8288 242.4082 148.5935 140.0095 Vwf 1.137470678 9.261835276 67.30309 2.33E-16 3.04E-13 238.1259 416.894 439.0121 483.1144 703.0341 781.5526 1009.109 826.6871 596.4043 Cyp26b1 1.902666438 7.003740301 63.6127 1.51E-15 1.80E-12 49.71186 48.22681 50.8566 62.81629 436.9736 202.4699 153.5869 175.0504 185.4672 Clca1 1.656827491 7.708517388 61.49154 4.45E-15 4.68E-12 73.03347 95.73914 99.46952 135.9116 325.0872 278.164 410.7987 306.6099 175.4665 Neo1 1.42976035 7.515603662 61.39479 4.67E-15 4.68E-12 75.48837 96.45361 138.3599 86.22964 211.4388 221.9739 280.0339 285.227 221.8333 Sdpr 1.065218748 8.649084093 60.3492 7.94E-15 7.40E-12 273.7221 279.3582 207.1658 253.5494 525.9541 577.2251 516.274 513.5535 452.7581 Cysltr2 1.489977987 7.031782927 46.59182 8.74E-12 7.60E-09 61.98639 68.232 78.52857 67.95581 181.485 158.3538 305.9402 202.5945 126.3723 Ehd3 2.093947543 6.144531981 46.01155 1.18E-11 9.58E-09 23.32161 29.29332 15.33177 34.26343 59.90767 124.9184 154.2037 82.63247 98.18851 Trim47 1.113919246 7.992941168 45.88583 1.25E-11 9.61E-09 133.1787 158.6126 201.9306 145.0485 225.5348 449.9848 391.6774 339.5904 222.7425 Nupr1 1.800700308 6.689482752 42.78297 6.12E-11 4.43E-08 47.87068 27.50714 39.26428 68.52687 142.7212 136.0635 115.9612 210.2054 144.5553 Hist1h1b -1.649263839 7.250135527 41.83442 9.93E-11 6.55E-08 190.869 192.1928 255.4048 303.8024 66.95563 51.54625 65.99919 84.08216 152.7377 Itga6 0.921088759 8.83691334 41.81089 1.01E-10 6.55E-08 252.8554 358.3073 321.2192 338.6369 621.1016 620.4125 663.076 583.5012 390.9357 Txnip 0.742730503 11.06909185 39.65004 3.04E-10 1.89E-07 1673.632 1596.486 1673.406 1408.227 2619.199 2779.318 2571.501 2812.766 2143.783 Dnm3 1.613578501 6.203760426 38.77882 4.75E-10 2.81E-07 59.53148 30.36503 24.30646 23.9844 96.02847 104.95 111.6435 75.02158 134.5546 Pbx3 1.396751546 5.89102973 37.83446 7.70E-10 4.37E-07 26.39024 37.86697 35.52483 26.83969 74.88459 88.23232 90.67179 82.27004 60.91324 Mt1 1.9308965 6.443307425 37.58785 8.74E-10 4.75E-07 39.89223 24.64926 24.30646 57.67678 131.2683 118.4171 104.8585 208.7557 102.7343 Itgb3 1.122377339 6.85528371 36.31597 1.68E-09 8.75E-07 66.28247 77.87736 67.68415 75.37955 96.02847 174.1427 148.6524 198.6079 121.8265 Sbspon 2.954911499 4.667150155 35.29356 2.84E-09 1.42E-06 1.84118 6.430241 7.852857 7.423744 24.66786 46.43806 71.55052 19.20842 33.63866 Cd53 2.874572428 5.473853479 34.71582 3.82E-09 1.84E-06 7.978446 2.857885 10.09653 38.26083 77.52758 71.05023 43.17704 52.18893 84.55122 Alcam 1.27213942 7.254424536 34.32926 4.65E-09 2.17E-06 82.85309 94.3102 102.0871 66.81369 114.5294 285.5941 189.3622 247.8974 167.2841 Ezh1 0.825459442 8.088786978 31.98716 1.55E-08 6.98E-06 211.122 205.0532 188.4686 168.4619 309.2293 399.8317 326.9119 343.9395 282.7466 Cpne8 1.000520255 6.63668724 30.70823 3.00E-08 1.30E-05 65.66875 61.08729 75.91095 53.10832 133.0303 126.7759 118.4285 127.5729 126.3723 Bcl3 1.954005889 5.731362414 30.42156 3.48E-08 1.46E-05 10.43335 18.57625 42.62979 22.84229 92.50449 72.44338 78.9523 88.79366 46.3668 Cldn5 1.94539843 5.697820109 29.9109 4.52E-08 1.84E-05 22.09416 17.14731 8.226802 31.9792 83.69454 80.33785 85.73727 63.78647 53.64002 Gstm2 1.986724271 4.899576256 29.73121 4.96E-08 1.96E-05 9.205899 16.79007 8.600748 9.707973 45.81175 34.82855 37.62571 55.45073 41.82103 Plscr2 1.815942575 6.440434218 29.40749 5.87E-08 2.25E-05 62.60011 38.22421 14.95782 29.12392 126.8633 131.8841 130.7648 131.5596 81.82376 Fyb 0.974496766 7.824584595 29.1213 6.80E-08 2.53E-05 127.6551 153.2541 173.5107 167.8908 251.0836 337.1403 177.0259 359.8861 258.1994 Exoc6b 1.259449662 6.776572269 28.30293 1.04E-07 3.76E-05 57.07657 95.38191 53.84816 53.10832 163.8651 135.1348 173.9418 130.4723 109.0983 Chrm3 1.676021298 5.684163295 27.9241 1.26E-07 4.45E-05 20.8667 32.1512 17.57544 23.41335 80.17056 81.26661 52.42926 101.116 42.73019 Ptger4 1.392819307 6.93874815 26.9574 2.08E-07 7.14E-05 40.50596 112.172 82.26802 46.82669 136.5543 172.7496 225.1374 135.9087 130.918 Tmpo -0.51529458 9.252002098 25.68822 4.01E-07 0.000134 716.219 773.7723 741.908 730.9532 483.6663 482.0271 528.6103 538.9232 480.0327 Ly6e 0.692185591 8.677454615 25.57611 4.25E-07 0.000139 357.1889 312.5812 251.2914 312.3683 484.5473 447.6629 490.9846 561.7558 455.4856 Jam2 1.629392728 5.399846561 25.39057 4.68E-07 0.000149 15.34317 12.50325 29.91565 22.27123 41.40677 57.11882 86.35408 69.22281 33.63866 Cpt1a 0.600623138 9.365794364 25.14859 5.31E-07 0.000165 527.1912 449.7596 598.6869 510.5252 740.0359 744.4021 786.439 873.4397 689.1379 Slco2a1 2.452350184 4.879647128 24.76083 6.49E-07 0.000194 9.819626 6.430241 2.617619 25.12652 44.93075 35.75731 49.34519 39.86654 40.00273 Tbc1d8 0.864003258 6.862580787 24.74741 6.54E-07 0.000194 66.28247 96.09638 72.17149 93.08233 139.1972 162.9976 139.4002 142.4323 125.4631 Fut8 0.790325773 8.941217707 24.63671 6.92E-07 0.000201 267.5848 435.4702 430.0374 280.9602 515.3822 623.6632 742.0283 651.6367 379.1168 Gm7694 1.094165478 6.068881667 24.48224 7.50E-07 0.00021 36.20987 42.51104 38.89034 50.25304 65.19364 94.26926 99.92401 76.47127 91.82444 Pam 1.006747199 7.510238343 24.46854 7.55E-07 0.00021 108.6296 96.81085 91.99061 90.7981 101.3144 80.33785 88.20453 86.2567 54.54917 Clu 1.518054887 7.161465215 24.24997 8.46E-07 0.00023 51.55304 81.80695 63.1968 114.2114 213.2008 120.2746 394.1447 189.5473 175.4665 Amotl2 0.94073062 6.687709469 24.12937 9.01E-07 0.00024 76.1021 58.2294 65.81442 76.52167 126.8633 143.0292 152.9701 111.9887 108.1892 Ccna2 -1.181249595 7.552003463 23.838 1.05E-06 0.000273 279.2456 267.5695 206.044 301.5182 107.4814 87.76794 83.27001 136.9959 268.2001 Dock9 0.826517277 8.2841484 22.98404 1.63E-06 0.000418 192.0964 166.4718 317.4798 224.9965 307.4673 398.9029 406.481 436.7198 306.3845 Oxr1 0.795219636 7.643107801 22.68137 1.91E-06 0.000479 109.8571 155.3975 199.6869 128.4879 210.5578 238.6916 269.5481 287.7639 180.9214 Pde3b 0.807164909 7.124360477 21.98419 2.75E-06 0.000676 77.32955 118.9595 107.3224 103.9324 138.3162 156.0319 228.2215 170.3389 143.6462 Cd38 1.605010427 5.103047708 21.71588 3.16E-06 0.000763 19.02552 6.430241 19.07122 16.56066 50.21672 45.97368 46.87793 42.4035 50.91256 Kif4 -1.206444065 5.781783747 21.38194 3.76E-06 0.000892 94.5139 62.51623 58.33551 90.7981 47.57374 20.43275 32.07437 27.18173 56.36748 Eltd1 0.577195942 11.34605073 21.30477 3.92E-06 0.000912 2114.288 1765.458 1986.399 2440.699 3050.886 2705.481 3047.682 3494.846 2785.644 Stat3 0.596339236 9.015147571 21.16878 4.21E-06 0.00095 444.3381 373.3112 375.8153 447.1378 557.6699 640.3809 645.1884 675.9191 479.1236 Fam63a 0.910075052 7.468203199 21.15865 4.23E-06 0.00095 115.9943 141.8225 112.5576 114.2114 266.9415 205.2562 294.2207 185.1982 170.9207 Flt3 -1.158717079 5.744662099 20.92357 4.78E-06 0.001056 108.6296 50.37022 71.04966 65.10052 55.5027 35.29293 21.58852 21.38296 48.1851 Rdh10 1.207502163 6.108998588 20.85805 4.95E-06 0.001075 22.70788 54.29981 45.24741 41.68718 90.7425 75.22966 101.7745 81.90762 100.0068 Mcm6 -0.561061782 8.837436335 20.60304 5.65E-06 0.001208 478.7068 580.1506 610.2792 591.0442 407.0198 348.7498 384.8925 392.1418 308.2028 Lamp2 0.50808363 9.246462645 20.53915 5.84E-06 0.001229 484.2303 462.2629 513.4273 536.2227 710.0821 773.1937 684.6645 623.0053 668.2274 Arhgap29 0.622903496 7.775368774 20.44655 6.13E-06 0.001269 199.4611 141.1081 158.179 178.7409 271.3465 237.7629 249.1932 267.4682 263.6543 Gem 1.731489668 5.2787729 20.38674 6.33E-06 0.001289 11.04708 26.43544 10.09653 23.41335 47.57374 94.26926 45.6443 47.115 32.7295 Pf4 -2.282102665 5.3882272 20.09661 7.36E-06 0.001477 49.71186 28.93608 97.22585 129.63 24.66786 10.21637 5.551334 20.65812 16.36475 Tm4sf1 1.859639574 4.797373024 19.93135 8.03E-06 0.001586 17.79807 12.14601 2.991565 7.994801 62.55066 23.68341 39.47615 26.09446 50.91256 Mpp6 0.668948694 8.181506644 19.88342 8.23E-06 0.001602 183.5043 255.0662 264.7535 187.3068 347.1121 357.5731 334.3137 334.8789 335.4774 Il2rg 0.793691426 7.419931714 19.84483 8.40E-06 0.00161 111.0845 156.112 126.3936 90.22704 196.4619 233.1191 217.1188 213.8296 163.6475 Myof 0.698368797 8.556756247 19.08939 1.25E-05 0.002352 300.1123 250.7794 332.8116 259.831 421.1157 588.3702 381.8084 493.6203 389.1174 Prcp 0.760233643 8.286128561 19.06579 1.26E-05 0.002352 179.8219 223.9867 227.7329 320.3631 353.2791 469.0244 407.7146 295.0124 318.2035 Tgm2 0.754133733 8.733450359 18.81857 1.44E-05 0.002636 262.0613 414.7505 295.7909 304.3735 560.3129 475.9901 636.5529 500.5063 362.752 Kif20b -0.850552858 6.937270924 18.7949 1.46E-05 0.002636 155.2728 170.0442 145.8388 171.8882 89.86151 127.7047 68.46645 80.82035 84.55122 Trpc1 1.29275328 5.246022046 18.49351 1.70E-05 0.002986 33.14124 16.43284 12.71415 18.27383 76.64658 29.72036 46.26112 46.75258 57.27663 Prtn3 1.007250399 7.013211632 18.46876 1.73E-05 0.002986 90.21781 73.2333 127.5154 51.39515 170.0321 147.2087 138.7833 183.3861 160.9201 Ckap2 -1.361421951 5.42060272 18.46285 1.73E-05 0.002986 53.39422 50.72746 65.44047 77.09272 30.83483 22.29027 17.27082 17.75873 42.73019 Rbpms2 1.107090799 5.714331786 18.43702 1.76E-05 0.002986 33.75496 29.29332 33.6551 27.41075 60.78867 50.15311 67.84964 64.14889 100.0068 Tc2n 3.06780076 5.054891298 18.42889 1.76E-05 0.002986 2.454906 2.857885 43.75163 7.994801 62.55066 54.79691 75.86823 15.58419 35.45696 Dtl -1.12850125 6.396918587 18.38655 1.80E-05 0.003014 119.6767 106.4562 97.59979 178.1699 54.6217 32.50664 53.66289 60.52466 77.278 Kntc1 -0.811676509 6.335580931 18.28899 1.90E-05 0.003132 143.612 95.73914 88.6251 79.94801 85.45653 51.54625 51.81245 40.95381 84.55122 Ptplad2 0.79465279 7.198711495 18.19318 2.00E-05 0.003253 109.8571 108.5996 112.5576 91.94021 175.318 204.3275 225.1374 144.6068 139.1004 Stom 1.76294122 4.476532486 17.96855 2.25E-05 0.003615 4.909813 7.144712 12.71415 15.41854 23.78687 26.46969 44.41067 35.51746 20.00136 Adamts6 -1.046952604 6.021058704 17.8803 2.35E-05 0.003654 116.6081 72.51883 93.11245 77.66378 36.1208 57.5832 32.07437 45.66531 41.82103 Plcg2 0.537912786 9.367650133 17.8802 2.35E-05 0.003654 437.5871 678.3904 575.5022 489.9671 560.3129 880.0013 759.9159 869.8154 655.4992 Ckap2l -1.01209639 6.445919331 17.87916 2.35E-05 0.003654 133.1787 85.02208 109.1921 149.0459 60.78867 61.29824 54.89652 38.41685 90.00614 Eps8 0.916803322 6.702211746 17.8532 2.39E-05 0.00366 68.12365 74.30501 72.54544 69.09792 156.8171 112.3801 183.8108 114.5257 101.8251 Osmr 1.72201522 5.213314224 17.44056 2.96E-05 0.004494 19.63925 10.35983 13.83599 23.41335 33.47782 32.97102 104.8585 61.61193 54.54917 Clip3 1.201930161 5.954315698 17.41307 3.01E-05 0.004507 57.6903 27.14991 25.05435 36.54766 53.74071 73.37214 91.2886 90.60578 94.5519 BC005764 0.922895619 6.291814721 17.37751 3.06E-05 0.00454 55.84912 50.37022 58.33551 46.82669 81.93255 120.739 76.48504 121.7742 80.91461 Dennd4a 0.483601905 8.627679179 17.24944 3.28E-05 0.004802 317.9104 309.366 351.5088 344.3475 385.8759 476.4545 499.0032 457.7404 401.8456 Lipt1 1.900207837 4.255928842 17.19748 3.37E-05 0.00488 6.137266 11.0743 5.983129 6.281629 21.14388 19.96837 23.43896 40.22896 28.18374
