Integrated analysis of an in vivo model of intra-nasal exposure to instilled air pollutants reveals cell-type specific responses in the placenta.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
19 05 2022
19 05 2022
Historique:
received:
05
01
2022
accepted:
06
05
2022
entrez:
19
5
2022
pubmed:
20
5
2022
medline:
24
5
2022
Statut:
epublish
Résumé
The placenta is a heterogeneous organ whose development involves complex interactions of trophoblasts with decidual, vascular, and immune cells at the fetal-maternal interface. It maintains a critical balance between maternal and fetal homeostasis. Placental dysfunction can lead to adverse pregnancy outcomes including intra-uterine growth restriction, pre-eclampsia, or pre-term birth. Exposure to environmental pollutants contributes to the development of placental abnormalities, with poorly understood molecular underpinning. Here we used a mouse (C57BL/6) model of environmental pollutant exposure by administration of a particulate matter (SRM1649b at 300 μg/day/mouse) suspension intra-nasally beginning 2 months before conception and during gestation, in comparison to saline-exposed controls. Placental transcriptomes, at day 19 of gestation, were determined using bulk RNA-seq from whole placentas of exposed (n = 4) and control (n = 4) animals and scRNAseq of three distinct placental layers, followed by flow cytometry analysis of the placental immune cell landscape. Our results indicate a reduction in vascular placental cells, especially cells responsible for structural integrity, and increase in trophoblast proliferation in animals exposed to particulate matter. Pollution-induced inflammation was also evident, especially in the decidual layer. These data indicate that environmental exposure to air pollutants triggers changes in the placental cellular composition, mediating adverse pregnancy outcomes.
Identifiants
pubmed: 35589747
doi: 10.1038/s41598-022-12340-z
pii: 10.1038/s41598-022-12340-z
pmc: PMC9119931
doi:
Substances chimiques
Air Pollutants
0
Particulate Matter
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
8438Subventions
Organisme : NIH HHS
ID : HD81206
Pays : United States
Organisme : NICHD NIH HHS
ID : R01 HD041230
Pays : United States
Organisme : NICHD NIH HHS
ID : R01 HD100015
Pays : United States
Organisme : NCI NIH HHS
ID : P30 CA016042
Pays : United States
Organisme : NIH HHS
ID : HD41230
Pays : United States
Organisme : NICHD NIH HHS
ID : R01 HD081206
Pays : United States
Organisme : NICHD NIH HHS
ID : R01 HD089714
Pays : United States
Informations de copyright
© 2022. The Author(s).
Références
Maltepe, E. & Fisher, S. J. Placenta: The forgotten organ. Annu. Rev. Cell Dev. Biol. 31, 523–552. https://doi.org/10.1146/annurev-cellbio-100814-125620 (2015).
doi: 10.1146/annurev-cellbio-100814-125620
pubmed: 26443191
Woods, L., Perez-Garcia, V. & Hemberger, M. Regulation of placental development and its impact on fetal growth-new insights from mouse models. Front. Endocrinol. Lausanne 9, 570. https://doi.org/10.3389/fendo.2018.00570 (2018).
doi: 10.3389/fendo.2018.00570
pubmed: 30319550
pmcid: 6170611
Cao, C. & Fleming, M. D. The placenta: The forgotten essential organ of iron transport. Nutr. Rev. 74, 421–431. https://doi.org/10.1093/nutrit/nuw009 (2016).
doi: 10.1093/nutrit/nuw009
pubmed: 27261274
pmcid: 5059819
Hoo, R., Nakimuli, A. & Vento-Tormo, R. Innate immune mechanisms to protect against infection at the human decidual–placental interface. Front. Immunol. 11, 2070. https://doi.org/10.3389/fimmu.2020.02070 (2020).
doi: 10.3389/fimmu.2020.02070
pubmed: 33013876
pmcid: 7511589
Zanini, M. J. et al. Urban-related environmental exposures during pregnancy and placental development and preeclampsia: A review. Curr. Hypertens. Rep. 22, 81. https://doi.org/10.1007/s11906-020-01088-4 (2020).
doi: 10.1007/s11906-020-01088-4
pubmed: 32880755
Lee, P. C., Roberts, J. M., Catov, J. M., Talbott, E. O. & Ritz, B. First trimester exposure to ambient air pollution, pregnancy complications and adverse birth outcomes in Allegheny County, PA. Matern. Child Health J. 17, 545–555. https://doi.org/10.1007/s10995-012-1028-5 (2013).
doi: 10.1007/s10995-012-1028-5
pubmed: 22544506
pmcid: 3636771
Melody, S. M. et al. Maternal exposure to ambient air pollution and pregnancy complications in Victoria, Australia. Int. J. Environ. Res. Public Health 17, 2572. https://doi.org/10.3390/ijerph17072572 (2020).
doi: 10.3390/ijerph17072572
pmcid: 7178226
Liu, Y., Xu, J., Chen, D., Sun, P. & Ma, X. The association between air pollution and preterm birth and low birth weight in Guangdong, China. BMC Public Health 19, 3. https://doi.org/10.1186/s12889-018-6307-7 (2019).
doi: 10.1186/s12889-018-6307-7
pubmed: 30606145
pmcid: 6318948
Rammah, A., Whitworth, K. W. & Symanski, E. Particle air pollution and gestational diabetes mellitus in Houston, Texas. Environ. Res. 190, 109988. https://doi.org/10.1016/j.envres.2020.109988 (2020).
doi: 10.1016/j.envres.2020.109988
pubmed: 32745750
Bearblock, E., Aiken, C. E. & Burton, G. J. Air pollution and pre-eclampsia; associations and potential mechanisms. Placenta 104, 188–194. https://doi.org/10.1016/j.placenta.2020.12.009 (2021).
doi: 10.1016/j.placenta.2020.12.009
pubmed: 33360680
Erickson, A. C. & Arbour, L. The shared pathoetiological effects of particulate air pollution and the social environment on fetal–placental development. J. Environ. Public Health 2014, 901017. https://doi.org/10.1155/2014/901017 (2014).
doi: 10.1155/2014/901017
pubmed: 25574176
pmcid: 4276595
Liu, Y., Wang, L., Wang, F. & Li, C. Effect of fine particulate matter (PM2.5) on rat placenta pathology and perinatal outcomes. Med. Sci. Monit. 22, 3274–3280. https://doi.org/10.12659/msm.897808 (2016).
doi: 10.12659/msm.897808
pubmed: 27629830
pmcid: 5036383
Goldman, S. L. et al. The impact of heterogeneity on single-cell sequencing. Front. Genet. 10, 8. https://doi.org/10.3389/fgene.2019.00008 (2019).
doi: 10.3389/fgene.2019.00008
pubmed: 30881372
pmcid: 6405636
Li, X. & Wang, C. Y. From bulk, single-cell to spatial RNA sequencing. Int. J. Oral Sci. 13, 36. https://doi.org/10.1038/s41368-021-00146-0 (2021).
doi: 10.1038/s41368-021-00146-0
pubmed: 34782601
pmcid: 8593179
Kuksin, M. et al. Applications of single-cell and bulk RNA sequencing in onco-immunology. Eur. J. Cancer 149, 193–210. https://doi.org/10.1016/j.ejca.2021.03.005 (2021).
doi: 10.1016/j.ejca.2021.03.005
pubmed: 33866228
Chen, G., Ning, B. & Shi, T. Single-cell RNA-Seq technologies and related computational data analysis. Front. Genet. 10, 317. https://doi.org/10.3389/fgene.2019.00317 (2019).
doi: 10.3389/fgene.2019.00317
pubmed: 31024627
pmcid: 6460256
Wang, X., He, Y., Zhang, Q., Ren, X. & Zhang, Z. Direct comparative analyses of 10X genomics chromium and Smart-seq2. Genomics Proteomics Bioinform. https://doi.org/10.1016/j.gpb.2020.02.005 (2021).
doi: 10.1016/j.gpb.2020.02.005
Aizarani, N. et al. A human liver cell atlas reveals heterogeneity and epithelial progenitors. Nature 572, 199–204. https://doi.org/10.1038/s41586-019-1373-2 (2019).
doi: 10.1038/s41586-019-1373-2
pubmed: 31292543
pmcid: 6687507
Park, J. et al. Single-cell transcriptomics of the mouse kidney reveals potential cellular targets of kidney disease. Science 360, 758–763. https://doi.org/10.1126/science.aar2131 (2018).
doi: 10.1126/science.aar2131
pubmed: 29622724
pmcid: 6188645
Del Vecchio, G. et al. Cell-free DNA methylation and transcriptomic signature prediction of pregnancies with adverse outcomes. Epigenetics 16, 1–20. https://doi.org/10.1080/15592294.2020.1816774 (2020).
doi: 10.1080/15592294.2020.1816774
Pique-Regi, R. et al. Single cell transcriptional signatures of the human placenta in term and preterm parturition. Elife 8, e52004. https://doi.org/10.7554/eLife.52004 (2019).
doi: 10.7554/eLife.52004
pubmed: 31829938
pmcid: 6949028
Suryawanshi, H. et al. A single-cell survey of the human first-trimester placenta and decidua. Sci. Adv. 4, eaau4788. https://doi.org/10.1126/sciadv.aau4788 (2018).
doi: 10.1126/sciadv.aau4788
pubmed: 30402542
pmcid: 6209386
Tabula Muris, C. et al. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris. Nature 562, 367–372. https://doi.org/10.1038/s41586-018-0590-4 (2018).
doi: 10.1038/s41586-018-0590-4
Vento-Tormo, R. et al. Single-cell reconstruction of the early maternal–fetal interface in humans. Nature 563, 347–353. https://doi.org/10.1038/s41586-018-0698-6 (2018).
doi: 10.1038/s41586-018-0698-6
pubmed: 30429548
pmcid: 7612850
Watson, E. D. & Cross, J. C. Development of structures and transport functions in the mouse placenta. Physiology (Bethesda) 20, 180–193. https://doi.org/10.1152/physiol.00001.2005 (2005).
doi: 10.1152/physiol.00001.2005
Ganguly, A., Touma, M., Thamotharan, S., De Vivo, D. C. & Devaskar, S. U. Maternal calorie restriction causing uteroplacental insufficiency differentially affects mammalian placental glucose and leucine transport molecular mechanisms. Endocrinology 157, 4041–4054. https://doi.org/10.1210/en.2016-1259 (2016).
doi: 10.1210/en.2016-1259
pubmed: 27494059
pmcid: 5045505
Brodsky, D. & Christou, H. Current concepts in intrauterine growth restriction. J. Intensive Care Med. 19, 307–319. https://doi.org/10.1177/0885066604269663 (2004).
doi: 10.1177/0885066604269663
pubmed: 15523117
Stergiou, E. et al. Effect of gestational diabetes and intrauterine growth restriction on the offspring’s circulating galanin at birth. J. Clin. Endocrinol. Metab. 97, E238–242. https://doi.org/10.1210/jc.2011-1855 (2012).
doi: 10.1210/jc.2011-1855
pubmed: 22162474
Blum, J. L., Chen, L. C. & Zelikoff, J. T. Exposure to ambient particulate matter during specific gestational periods produces adverse obstetric consequences in mice. Environ. Health Perspect. 125, 077020. https://doi.org/10.1289/EHP1029 (2017).
doi: 10.1289/EHP1029
pubmed: 28893721
pmcid: 5744697
Veras, M. M. et al. Particulate urban air pollution affects the functional morphology of mouse placenta. Biol. Reprod. 79, 578–584. https://doi.org/10.1095/biolreprod.108.069591 (2008).
doi: 10.1095/biolreprod.108.069591
pubmed: 18509159
Wei, L. https://doi.org/10.6084/m9.figshare.6025748 (2020).
Nadel, B. B. et al. The Gene Expression Deconvolution Interactive Tool (GEDIT): Accurate cell type quantification from gene expression data. Gigascience 10, giab002. https://doi.org/10.1093/gigascience/giab002 (2021).
doi: 10.1093/gigascience/giab002
pubmed: 33590863
pmcid: 7931818
Nadel, B. B. et al. Systematic evaluation of transcriptomics-based deconvolution methods and references using thousands of clinical samples. Brief Bioinform. 22, bbab265. https://doi.org/10.1093/bib/bbab265 (2021).
doi: 10.1093/bib/bbab265
pubmed: 34346485
pmcid: 8768458
Wang, X., Park, J., Susztak, K., Zhang, N. R. & Li, M. Bulk tissue cell type deconvolution with multi-subject single-cell expression reference. Nat. Commun. 10, 380. https://doi.org/10.1038/s41467-018-08023-x (2019).
doi: 10.1038/s41467-018-08023-x
pubmed: 30670690
pmcid: 6342984
Liberzon, A. et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 1, 417–425. https://doi.org/10.1016/j.cels.2015.12.004 (2015).
doi: 10.1016/j.cels.2015.12.004
pubmed: 26771021
pmcid: 4707969
Familari, M. et al. Exposure of trophoblast cells to fine particulate matter air pollution leads to growth inhibition, inflammation and ER stress. PLoS ONE 14, e0218799. https://doi.org/10.1371/journal.pone.0218799 (2019).
doi: 10.1371/journal.pone.0218799
pubmed: 31318865
pmcid: 6638881
Naav, A. et al. Urban PM2.5 induces cellular toxicity, hormone dysregulation, oxidative damage, inflammation, and mitochondrial interference in the HRT8 trophoblast cell line. Front. Endocrinol. Lausanne 11, 75. https://doi.org/10.3389/fendo.2020.00075 (2020).
doi: 10.3389/fendo.2020.00075
pubmed: 32226408
pmcid: 7080655
Han, X. et al. Construction of a human cell landscape at single-cell level. Nature 581, 303–309. https://doi.org/10.1038/s41586-020-2157-4 (2020).
doi: 10.1038/s41586-020-2157-4
pubmed: 32214235
Li, H., Huang, Q., Liu, Y. & Garmire, L. X. Single cell transcriptome research in human placenta. Reproduction 160, R155–R167. https://doi.org/10.1530/REP-20-0231 (2020).
doi: 10.1530/REP-20-0231
pubmed: 33112783
pmcid: 7707799
Khan, T. et al. Single nucleus RNA sequence (snRNAseq) analysis of the spectrum of trophoblast lineages generated from human pluripotent stem cells in vitro. Front. Cell Dev. Biol. 9, 695248. https://doi.org/10.3389/fcell.2021.695248 (2021).
doi: 10.3389/fcell.2021.695248
pubmed: 34368143
pmcid: 8334858
Pollheimer, J., Vondra, S., Baltayeva, J., Beristain, A. G. & Knofler, M. Regulation of placental extravillous trophoblasts by the maternal uterine environment. Front. Immunol. 9, 2597. https://doi.org/10.3389/fimmu.2018.02597 (2018).
doi: 10.3389/fimmu.2018.02597
pubmed: 30483261
pmcid: 6243063
Ramhorst, R. et al. Decoding the chemokine network that links leukocytes with decidual cells and the trophoblast during early implantation. Cell Adhes. Migr. 10, 197–207. https://doi.org/10.1080/19336918.2015.1135285 (2016).
doi: 10.1080/19336918.2015.1135285
Solders, M. et al. Recruitment of MAIT cells to the intervillous space of the placenta by placenta-derived chemokines. Front. Immunol. 10, 1300. https://doi.org/10.3389/fimmu.2019.01300 (2019).
doi: 10.3389/fimmu.2019.01300
pubmed: 31244846
pmcid: 6563723
Schumacher, A., Sharkey, D. J., Robertson, S. A. & Zenclussen, A. C. Immune cells at the fetomaternal interface: How the microenvironment modulates immune cells to foster fetal development. J. Immunol. 201, 325–334. https://doi.org/10.4049/jimmunol.1800058 (2018).
doi: 10.4049/jimmunol.1800058
pubmed: 29987001
Yang, F., Zheng, Q. & Jin, L. Dynamic function and composition changes of immune cells during normal and pathological pregnancy at the maternal–fetal interface. Front. Immunol. 10, 2317. https://doi.org/10.3389/fimmu.2019.02317 (2019).
doi: 10.3389/fimmu.2019.02317
pubmed: 31681264
pmcid: 6813251
Cohen, B. M. & Machupalli, S. Use of gammaglobulin to lower elevated natural killer cells in patients with recurrent miscarriage. J. Reprod. Med. 60, 294–300 (2015).
pubmed: 26380487
Dosiou, C. & Giudice, L. C. Natural killer cells in pregnancy and recurrent pregnancy loss: Endocrine and immunologic perspectives. Endocr. Rev. 26, 44–62. https://doi.org/10.1210/er.2003-0021 (2005).
doi: 10.1210/er.2003-0021
pubmed: 15689572
Jeve, Y. B. & Davies, W. Evidence-based management of recurrent miscarriages. J. Hum. Reprod. Sci. 7, 159–169. https://doi.org/10.4103/0974-1208.142475 (2014).
doi: 10.4103/0974-1208.142475
pubmed: 25395740
pmcid: 4229790
Sharma, S. Natural killer cells and regulatory T cells in early pregnancy loss. Int. J. Dev. Biol. 58, 219–229. https://doi.org/10.1387/ijdb.140109ss (2014).
doi: 10.1387/ijdb.140109ss
pubmed: 25023688
pmcid: 4306453
Taylor, E. B. & Sasser, J. M. Natural killer cells and T lymphocytes in pregnancy and pre-eclampsia. Clin. Sci. (Lond.) 131, 2911–2917. https://doi.org/10.1042/CS20171070 (2017).
doi: 10.1042/CS20171070
Veljkovic Vujaklija, D., Sucic, S., Gulic, T., Dominovic, M. & Rukavina, D. Cell death mechanisms at the maternal–fetal interface: Insights into the role of granulysin. Clin. Dev. Immunol. 2012, 180272. https://doi.org/10.1155/2012/180272 (2012).
doi: 10.1155/2012/180272
pubmed: 21912564
Yougbare, I. et al. Activated NK cells cause placental dysfunction and miscarriages in fetal alloimmune thrombocytopenia. Nat. Commun. 8, 224. https://doi.org/10.1038/s41467-017-00269-1 (2017).
doi: 10.1038/s41467-017-00269-1
pubmed: 28794456
pmcid: 5550461
Brown, M. B., von Chamier, M., Allam, A. B. & Reyes, L. M1/M2 macrophage polarity in normal and complicated pregnancy. Front. Immunol. 5, 606. https://doi.org/10.3389/fimmu.2014.00606 (2014).
doi: 10.3389/fimmu.2014.00606
pubmed: 25505471
pmcid: 4241843
Kim, C. J., Romero, R., Chaemsaithong, P. & Kim, J. S. Chronic inflammation of the placenta: Definition, classification, pathogenesis, and clinical significance. Am. J. Obstet. Gynecol. 213, S53-69. https://doi.org/10.1016/j.ajog.2015.08.041 (2015).
doi: 10.1016/j.ajog.2015.08.041
pubmed: 26428503
pmcid: 4782598
Vu, M. D. et al. Critical, but conditional, role of OX40 in memory T cell-mediated rejection. J. Immunol. 176, 1394–1401. https://doi.org/10.4049/jimmunol.176.3.1394 (2006).
doi: 10.4049/jimmunol.176.3.1394
pubmed: 16424166
Derricott, H. et al. Characterizing villitis of unknown etiology and inflammation in stillbirth. Am. J. Pathol. 186, 952–961. https://doi.org/10.1016/j.ajpath.2015.12.010 (2016).
doi: 10.1016/j.ajpath.2015.12.010
pubmed: 26851347
Li, X. C., Kloc, M. & Ghobrial, R. M. Memory T cells in transplantation—Progress and challenges. Curr. Opin. Organ Transplant. 18, 387–392. https://doi.org/10.1097/MOT.0b013e3283626130 (2013).
doi: 10.1097/MOT.0b013e3283626130
pubmed: 23838642
pmcid: 4264634
Valujskikh, A. & Li, X. C. Frontiers in nephrology: T cell memory as a barrier to transplant tolerance. J. Am. Soc. Nephrol. 18, 2252–2261. https://doi.org/10.1681/ASN.2007020151 (2007).
doi: 10.1681/ASN.2007020151
pubmed: 17634436
Chaouat, G., Petitbarat, M., Dubanchet, S., Rahmati, M. & Ledee, N. Tolerance to the foetal allograft?. Am. J. Reprod. Immunol. 63, 624–636. https://doi.org/10.1111/j.1600-0897.2010.00832.x (2010).
doi: 10.1111/j.1600-0897.2010.00832.x
pubmed: 20367624
PrabhuDas, M. et al. Immune mechanisms at the maternal–fetal interface: Perspectives and challenges. Nat. Immunol. 16, 328–334. https://doi.org/10.1038/ni.3131 (2015).
doi: 10.1038/ni.3131
pubmed: 25789673
pmcid: 5070970
Viola, A., Munari, F., Sanchez-Rodriguez, R., Scolaro, T. & Castegna, A. The metabolic signature of macrophage responses. Front. Immunol. 10, 1462. https://doi.org/10.3389/fimmu.2019.01462 (2019).
doi: 10.3389/fimmu.2019.01462
pubmed: 31333642
pmcid: 6618143
Lu, F. et al. The effect of over-expression of sFlt-1 on blood pressure and the occurrence of other manifestations of preeclampsia in unrestrained conscious pregnant mice. Am. J. Obstet. Gynecol. 196, 396 e391–397; discussion 396 e397. https://doi.org/10.1016/j.ajog.2006.12.024 (2007).
doi: 10.1016/j.ajog.2006.12.024
Ganguly, A. et al. Glucose transporter isoform-3 mutations cause early pregnancy loss and fetal growth restriction. Am. J. Physiol. Endocrinol. Metab. 292, E1241-1255. https://doi.org/10.1152/ajpendo.00344.2006 (2007).
doi: 10.1152/ajpendo.00344.2006
pubmed: 17213475
Marx, V. Method of the year: Spatially resolved transcriptomics. Nat. Methods 18, 9–14. https://doi.org/10.1038/s41592-020-01033-y (2021).
doi: 10.1038/s41592-020-01033-y
pubmed: 33408395
Soto, S. F. et al. Exposure to fine particulate matter in the air alters placental structure and the renin–angiotensin system. PLoS ONE 12, e0183314. https://doi.org/10.1371/journal.pone.0183314 (2017).
doi: 10.1371/journal.pone.0183314
pubmed: 28820906
pmcid: 5562329
Weldy, C. S., Liu, Y., Liggitt, H. D. & Chin, M. T. In utero exposure to diesel exhaust air pollution promotes adverse intrauterine conditions, resulting in weight gain, altered blood pressure, and increased susceptibility to heart failure in adult mice. PLoS ONE 9, e88582. https://doi.org/10.1371/journal.pone.0088582 (2014).
doi: 10.1371/journal.pone.0088582
pubmed: 24533117
pmcid: 3922927
Behlen, J. C. et al. Gestational exposure to ultrafine particles reveals sex- and dose-specific changes in offspring birth outcomes, placental morphology, and gene networks. Toxicol. Sci. 184, 204–213. https://doi.org/10.1093/toxsci/kfab118 (2021).
doi: 10.1093/toxsci/kfab118
pubmed: 34609516
Ye, X. et al. Developing brain glucose transporters, serotonin, serotonin transporter, and oxytocin receptor expression in response to early-life hypocaloric and hypercaloric dietary, and air pollutant exposures. Dev. Neurosci. 43, 1–16. https://doi.org/10.1159/000514709 (2021).
doi: 10.1159/000514709
Percie du Sert, N. et al. The ARRIVE guidelines 2.0: Updated guidelines for reporting animal research. PLoS Biol 18, e3000410. https://doi.org/10.1371/journal.pbio.3000410 (2020).
doi: 10.1371/journal.pbio.3000410
pubmed: 32663219
pmcid: 7360023
Andrews, S. FastQC: A quality control tool for high throughput sequence data. Available online at: http://www.bioinformaticsbabrahamacuk/projects/fastqc/ (2010).
Dobin, A. et al. STAR: Ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21. https://doi.org/10.1093/bioinformatics/bts635 (2013).
doi: 10.1093/bioinformatics/bts635
pubmed: 23104886
Liao, Y., Smyth, G. K. & Shi, W. featureCounts: An efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930. https://doi.org/10.1093/bioinformatics/btt656 (2014).
doi: 10.1093/bioinformatics/btt656
pubmed: 24227677
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550. https://doi.org/10.1186/s13059-014-0550-8 (2014).
doi: 10.1186/s13059-014-0550-8
pubmed: 25516281
pmcid: 4302049
Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420. https://doi.org/10.1038/nbt.4096 (2018).
doi: 10.1038/nbt.4096
pubmed: 29608179
pmcid: 6700744
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 e1821. https://doi.org/10.1016/j.cell.2019.05.031 (2019).
doi: 10.1016/j.cell.2019.05.031
pubmed: 31178118
pmcid: 6687398
Marsh, B. & Blelloch, R. Single nuclei RNA-seq of mouse placental labyrinth development. Elife 9, e60266. https://doi.org/10.7554/eLife.60266 (2020).
doi: 10.7554/eLife.60266
pubmed: 33141023
pmcid: 7669270
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).
doi: 10.1007/978-3-319-24277-4
Wu, T. et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innovation (N. Y.) 2, 100141. https://doi.org/10.1016/j.xinn.2021.100141 (2021).
doi: 10.1016/j.xinn.2021.100141
Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847–2849. https://doi.org/10.1093/bioinformatics/btw313 (2016).
doi: 10.1093/bioinformatics/btw313
pubmed: 27207943