Home Food Science and Human Wellness Article
PDF (1.5 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript
Show Outline
Figures (2)

Tables (1)
Table 1
Review Article | Open Access

Food nutrition and toxicology targeting on specific organs in the era ofsingle-cell sequencing

Xiaofei Wang, Xiaowen Cheng, Huiling Liu, Xiaohuan Mu, Hao Zheng()
National Engineering Technology Research Center for Fruit and Vegetable Processing, Key Open Laboratory of Fruit and Vegetable Processing, Ministry of Agriculture and Rural Affairs, Beijing Key Laboratory of Food Non-Thermal Processing, College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China

Peer review under responsibility of Tsinghua University Press.

Show Author Information

Abstract

Due to the complex natures of dietary food components, it is difficult to elucidate how the compounds affect host health. Dietary food often selectively presents its mechanism of action on different cell types, and participates in the modulation of targeted cells and their microenvironments within organs. However, the limitations of traditional in vitro assays or in vivo animal experiments cannot comprehensively examine cellular heterogeneity and the tissue-biased influences. Single-cell RNA sequencing (scRNA-seq) has emerged as an indispensable methodology to decompose tissues into different cell types for the demonstration of transcriptional profiles of individual cells. ScRNA-seq applications has been summarized on three typical organs (brain, liver, kidney), and two representative immune-and tumor related health problems. The everincreasing role of scRNA-seq in dietary food research with further improvement can provide sub-cellular information and the coupling between other cellular modalities. In this review, we propose utilizing scRNAseq to more effectively capture the subtle and complex effects of food chemicals, and how they may lead to health problems at single-cell resolution. This novel technique will be valuable to elucidate the underlying mechanism of both the health benefits of food nutrients and the detrimental consequences food toxicants at the cellular level.

Keywords

Dietary foodCellular heterogeneitySingle-cell RNA sequencingFood nutrientsFood toxicants

References

[1]

A. Afshin, P.J. Sur, K.A. Fay, et al., Health effects of dietary risks in 195 countries, 1990–2017: a systematic analysis for the global burden of disease study 2017, Lancet 393 (2019) 1958–1972. https://doi.org/10.1016/S0140-6736(19)30041-8.

Crossref Google Scholar
[2]

C. Sapienza, J.P. Issa, Diet, nutrition, and cancer epigenetics, Annu. Rev. Nutr. 36 (2016) 665–681. https://doi.org/10.1146/annurevnutr-121415-112634.

Crossref Google Scholar
[3]

D.P. Jones, Y. Park, T.R. Ziegler, Nutritional metabolomics: progress in addressing complexity in diet and health, Annu. Rev. Nutr. 32 (2012) 183–202. https://doi.org/10.1146/annurev-nutr-072610-145159.

Crossref Google Scholar
[4]

K.S. Vimaleswaran, C.I. Le Roy, S.P. Claus, Foodomics for personalized nutrition: how far are we?, Curr. Opin. Food Sci. 4 (2015) 129–135. https://doi.org/10.1016/j.cofs.2015.07.001.

Crossref Google Scholar
[5]

F. Gómez-Pinilla, Brain foods: The effects of nutrients on brain function, Nat. Rev. Neurosci. 9 (2008) 568–578. http://doi.org/10.1038/nrn2421.

Crossref Google Scholar
[6]

M. Milošević, A. Arsić, Z. Cvetković, et al., Memorable food: fighting agerelated neurodegeneration by precision nutrition, Front. Nutr. 8 (2021) 1–13. https://doi.org/10.3389/fnut.2021.688086.

Crossref Google Scholar
[7]

J.M. Bourre, Roles of unsaturated fatty acids (especially omega-3 fatty acids) in the brain at various ages and during ageing, J. Nutr. Health Aging 8 (2004) 163–74.

Google Scholar
[8]

P. Hajeb, J.J. Sloth, S. Shakibazadeh, et al., Toxic elements in food: occurrence, binding, and reduction approaches, Compr. Rev. Food Sci. Food Saf. 13 (2014) 457–472. https://doi.org/10.1111/1541-4337.12068.

Crossref Google Scholar
[9]

H. Guo, J. Ji, J.S. Wang, et al., Co-contamination and interaction of fungal toxins and other environmental toxins, Trends Food Sci. Technol. 103 (2020) 162–178. https://doi.org/10.1016/j.tifs.2020.06.021.

Crossref Google Scholar
[10]

P. Erkekoglu, T. Baydar, Acrylamide neurotoxicity, Nutr. Neurosci. 17 (2014) 49–57. https://doi.org/10.1179/1476830513Y.0000000065.

Crossref Google Scholar
[11]

D. Wang, S. Bodovitz, Single cell analysis: the new frontier in “omics”, Trends Biotechnol. 28 (2010) 281–290. https://doi.org/10.1016/j.tibtech.2010.03.002.

Crossref Google Scholar
[12]

D.T. Paik, S. Cho, L. Tian, et al., Single-cell RNA sequencing in cardiovascular development, disease and medicine, Nat. Rev. Cardiol. 17(2020) 457–473. https://doi.org/10.1038/s41569-020-0359-y.

Crossref Google Scholar
[13]

Q. Zhang, W.M. Caudle, J. Pi, et al., Embracing systems toxicology at single-cell resolution, Curr. Opin. Toxicol. 16 (2019) 49–57. https://doi.org/10.1016/j.cotox.2019.04.003.

Crossref Google Scholar
[14]

R. Cuevas-Diaz Duran, H. Wei, J.Q. Wu, Single-cell RNA-sequencing of the brain, Clin. Transl. Med. 6 (2017) 20. https://doi.org/10.1186/s40169-017-0150-9.

Crossref Google Scholar
[15]

J. Kocot, D. Luchowska-Kocot, M. Kiełczykowska, et al., Does vitamin C influence neurodegenerative diseases and psychiatric disorders?, Nutrients 9(2017) 659. https://doi.org/10.3390/nu9070659.

Crossref Google Scholar
[16]

H.A. Park, M.M. Hayden, S. Bannerman, et al., Anti-apoptotic effects of carotenoids in neurodegeneration, Molecules 25 (2020) 1–19. https://doi.org/10.3390/molecules25153453.

Crossref Google Scholar
[17]

S. Petrovic, A. Arsic, D. Ristic-Medic, et al., Lipid peroxidation and antioxidant supplementation in neurodegenerative diseases: a review of human studies, Antioxidants 9 (2020) 1–27. https://doi.org/10.3390/antiox9111128.

Crossref Google Scholar
[18]

B.R. Cardoso, B.R. Roberts, A.I. Bush, et al., Selenium, selenoproteins and neurodegenerative diseases, Metallomics 7 (2015) 1213–1228. https://doi.org/10.1039/c5mt00075k.

Crossref Google Scholar
[19]

O.V. Forlenza, V.J.R. De-Paula, B.S.O. Diniz, Neuroprotective effects of lithium: Implications for the treatment of Alzheimer’s disease and related neurodegenerative disorders, ACS Chem. Neurosci. 5 (2014) 443–450. https://doi.org/10.1021/cn5000309.

Crossref Google Scholar
[20]

A. Ndayisaba, C. Kaindlstorfer, G.K. Wenning, Iron in neurodegeneration-cause or consequence?, Front. Neurosci. 13 (2019) 1–15. https://doi.org/10.3389/fnins.2019.00180.

Crossref Google Scholar
[21]

B. Szewczyk, Zinc homeostasis and neurodegenerative disorders, Front. Aging Neurosci. 5 (2013) 1–12. https://doi.org/10.3389/fnagi.2013.00033.

Crossref Google Scholar
[22]

B. Witt, D. Schaumlöffel, T. Schwerdtle, Subcellular localization of copper—cellular bioimaging with focus on neurological disorders, Int. J. Mol. Sci. 21 (2020) 10–12. https://doi.org/10.3390/ijms21072341.

Crossref Google Scholar
[23]

G.C. Román, R.E. Jackson, R. Gadhia, et al., Mediterranean diet: the role of long-chain ω-3 fatty acids in fish; polyphenols in fruits, vegetables, cereals, coffee, tea, cacao and wine; probiotics and vitamins in prevention of stroke, age-related cognitive decline, and Alzheimer disease, Rev. Neurol. (Paris) 175 (2019) 724–741. https://doi.org/10.1016/j.neurol.2019.08.005.

Crossref Google Scholar
[24]

S.C. Duplantier, C.D. Gardner, A critical review of the study of neuroprotective diets to reduce cognitive decline, Nutrients 13 (2021). https://doi.org/10.3390/nu13072264.

Crossref Google Scholar
[25]

G. Chen, B. Wang, D. Han, et al., Molecular mechanisms of the coordination between astaxanthin and fatty acid biosynthesis in Haematococcus pluvialis(Chlorophyceae), Plant J. 81 (2015) 95–107. https://doi.org/10.1111/tpj.12713.

Crossref Google Scholar
[26]

M. Das, S. Das, Docosahexaenoic acid (DHA) induced morphological differentiation of astrocytes is associated with transcriptional upregulation and endocytosis of β 2 -AR, Mol. Neurobiol. 56 (2019) 2685–2702. https://doi.org/10.1007/s12035-018-1260-0.

Crossref Google Scholar
[27]

J.Z. Yu, J. Wang, S.D. Sheridan, et al., N-3 polyunsaturated fatty acids promote astrocyte differentiation and neurotrophin production independent of cAMP in patient-derived neural stem cells, Mol. Psychiatry 26 (2021) 4605–4615. https://doi.org/10.1038/s41380-020-0786-5.

Crossref Google Scholar
[28]

B. Song, G. Xiong, H. Luo, et al., Single-cell RNA sequencing of mouse neural stem cell differentiation reveals adverse effects of cadmium on neurogenesis, Food Chem. Toxicol. 148 (2021) 111936. https://doi.org/10.1016/j.fct.2020.111936.

Crossref Google Scholar
[29]

D. Ofengeim, N. Giagtzoglou, D. Huh, et al., Single-cell RNA sequencing: unraveling the brain one cell at a time, Trends Mol. Med. 23 (2017) 563–576. https://doi.org/10.1016/j.molmed.2017.04.006.

Crossref Google Scholar
[30]

A. Zeisel, A.B. Muñoz-Manchado, S. Codeluppi, et al., Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq, Science 347 (2015) 1138–1142. https://doi.org/10.1126/science.aaa1934.

Crossref Google Scholar
[31]

R. Tarawneh, D.M. Holtzman, The clinical problem of symptomatic Alzheimer disease and mild cognitive impairment, Cold Spring Harb. Perspect. Med. 2 (2012) 1–16. https://doi.org/10.1101/cshperspect.a006148.

Crossref Google Scholar
[32]

N.C. Berchtold, P.D. Coleman, D.H. Cribbs, et al., Synaptic genes are extensively downregulated across multiple brain regions in normal human aging and Alzheimer’s disease, Neurobiol. Aging 34 (2013) 1653–1661. https://doi.org/10.1016/j.neurobiolaging.2012.11.024.

Crossref Google Scholar
[33]

M. Magistri, D. Velmeshev, M. Makhmutova, et al., Transcriptomics profiling of Alzheimer’s disease reveal neurovascular defects, altered amyloid-β homeostasis, and deregulated expression of long noncoding RNAs, J. Alzheimer’s Dis. 48 (2015) 647–665. https://doi.org/10.3233/JAD-150398.

Crossref Google Scholar
[34]

J.A. Miller, R.L. Woltjer, J.M. Goodenbour, S. Horvath, et al., Genes and pathways underlying regional and cell type changes in Alzheimer’s disease, Genome Med. 5 (2013) 48. https://doi.org/10.1186/gm452.

Crossref Google Scholar
[35]

A. Grubman, G. Chew, J.F. Ouyang, et al., A single-cell atlas of entorhinal cortex from individuals with Alzheimer’s disease reveals cell-type-specific gene expression regulation, Nat. Neurosci. 22 (2019) 2087–2097. https://doi.org/10.1038/s41593-019-0539-4.

Crossref Google Scholar
[36]

H. Mathys, J. Davila-Velderrain, Z. Peng, et al., Single-cell transcriptomic analysis of Alzheimer’s disease, Nature 570 (2019) 332–337. https://doi.org/10.1038/s41586-019-1195-2.

Crossref Google Scholar
[37]

J.M. Banales, R.C. Huebert, T. Karlsen, et al., Cholangiocyte pathobiology, Nat. Rev. Gastroenterol. Hepatol. 16 (2019) 269–281. https://doi.org/10.1038/s41575-019-0125-y.

Crossref Google Scholar
[38]

R.J. Schulze, M.B. Schott, C.A. Casey, et al., The cell biology of the hepatocyte: a membrane trafficking machine, J. Cell Biol. 218 (2019) 2096–2112. https://doi.org/10.1083/jcb.201903090.

Crossref Google Scholar
[39]

K.B. Halpern, R. Shenhav, O. Matcovitch-Natan, et al., Single-cell spatial reconstruction reveals global division of labour in the mammalian liver, Nature 542 (2017) 352–356. https://doi.org/10.1038/nature21065.

Crossref Google Scholar
[40]

P. Ramachandran, K.P. Matchett, R. Dobie, et al., Single-cell technologies in hepatology: new insights into liver biology and disease pathogenesis, Nat. Rev. Gastroenterol. Hepatol. 17 (2020) 457–472. https://doi.org/10.1038/s41575-020-0304-x.

Crossref Google Scholar
[41]

S.R. Park, C.S. Cho, J. Xi, et al., Holistic characterization of singlehepatocyte transcriptome responses to high-fat diet, Am. J. Physiol. - Endocrinol. Metab. 320 (2021) E244–E258. https://doi.org/10.1152/AJPENDO.00391.2020.

Crossref Google Scholar
[42]

L.M.S. Gerhardt, A.P. McMahon, Multi-omic approaches to acute kidney injury and repair, Curr. Opin. Biomed. Eng. 20 (2021) 100344. https://doi.org/10.1016/j.cobme.2021.100344.

Crossref Google Scholar
[43]

J. Park, R. Shrestha, C. Qiu, A. Kondo, et al., Single-cell transcriptomics of the mouse kidney reveals potential cellular targets of kidney disease, Science 360 (2018) 758–763. https://doi.org/10.1126/science.aar2131.

Crossref Google Scholar
[44]

J. Liao, Z. Yu, Y. Chen, et al., Single-cell RNA sequencing of human kidney, Sci. Data 7 (2020) 1–9. https://doi.org/10.1038/s41597-019-0351-8.

Crossref Google Scholar
[45]

H. Zhu, C. Cao, Z. Wu, et al., The probiotic L. casei Zhang slows the progression of acute and chronic kidney disease, Cell Metab. 33 (2021) 1926-1942. https://doi.org/10.1016/j.cmet.2021.06.014.

Crossref Google Scholar
[46]

M.K. Yadav, I. Kumari, B. Singh, et al., Probiotics, prebiotics and synbiotics: safe options for next-generation therapeutics, Appl. Microbiol. Biotechnol. 106 (2022) 505–521. https://doi.org/10.1007/s00253-021-11646-8.

Crossref Google Scholar
[47]

S. Raha, H.J. Lee, S. Yumnam, et al., Vitamin D2 suppresses amyloid-β 25–35 induced microglial activation in BV2 cells by blocking the NF-κB inflammatory signaling pathway, Life Sci. 161 (2016) 37–44. https://doi.org/10.1016/j.lfs.2016.07.017.

Crossref Google Scholar
[48]

P.C. Calder, Foods to deliver immune-supporting nutrients, Curr. Opin. Food Sci. 43 (2022) 136–145. https://doi.org/10.1016/j.cofs.2021.12.006.

Crossref Google Scholar
[49]

R. Thirumdas, A. Kothakota, R. Pandiselvam, et al., Role of food nutrients and supplementation in fighting against viral infections and boosting immunity: a review, Trends Food Sci. Technol. 110 (2021) 66–77. https://doi.org/10.1016/j.tifs.2021.01.069.

Crossref Google Scholar
[50]

M.J.T. Stubbington, O. Rozenblatt-Rosen, A. Regev, et al., Single-cell transcriptomics to explore the immune system in health and disease, Science 358(2017): 58-63 https://doi.org/10.1126/science.aan6828.

Crossref Google Scholar
[51]

Y. Elyahu, I. Hekselman, I. Eizenberg-Magar, et al., Aging promotes reorganization of the CD4 T cell landscape toward extreme regulatory and effector phenotypes, Sci. Adv. 5 (2019). https://doi.org/10.1126/sciadv.aaw8330.

Crossref Google Scholar
[52]

D.A. Jaitin, E. Kenigsberg, H. Keren-Shaul, et al., Massively parallel singlecell RNA-seq for marker-free decomposition of tissues into cell types, Science 343 (2014) 776–779. https://doi.org/10.1126/science.1247651.

Crossref Google Scholar
[53]

E. Papalexi, R. Satija, Single-cell RNA sequencing to explore immune cell heterogeneity, Nat. Rev. Immunol. 18 (2018) 35–45. https://doi.org/10.1038/nri.2017.76.

Crossref Google Scholar
[54]

C.S. Smillie, M. Biton, J. Ordovas-Montanes, et al., Intra-and inter-cellular rewiring of the human colon during ulcerative colitis, Cell 178 (2019) 714-730. https://doi.org/10.1016/j.cell.2019.06.029.

Crossref Google Scholar
[55]

M. Liao, Y. Liu, J. Yuan, et al., Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19, Nat. Med. 26 (2020) 842–844. https://doi.org/10.1038/s41591-020-0901-9.

Crossref Google Scholar
[56]

B. Lim, Y. Lin, N. Navin, Advancing cancer research and medicine with single-cell genomics, Cancer Cell 37 (2020) 456–470. https://doi.org/10.1016/j.ccell.2020.03.008.

Crossref Google Scholar
[57]

V. Bernard, A. Semaan, J. Huang, et al., Single-cell transcriptomics of pancreatic cancer precursors demonstrates epithelial and microenvironmental heterogeneity as an early event in neoplastic progression, Clin. Cancer Res. 25 (2019) 2194–2205. https://doi.org/10.1158/1078-0432.CCR-18-1955.

Crossref Google Scholar
[58]

M.G. Filbin, I. Tirosh, V. Hovestadt, et al., Developmental and oncogenic programs in H3K27M gliomas dissected by single-cell RNA-seq, Science 360 (2018) 331–335. https://doi.org/10.1126/science.aao4750.

Crossref Google Scholar
[59]

R. Kalluri, The biology and function of fibroblasts in cancer, Nat. Rev. Cancer 16 (2016) 582–598. https://doi.org/10.1038/nrc.2016.73.

Crossref Google Scholar
[60]

A.S. Venteicher, I. Tirosh, C. Hebert, et al., Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq, Science 355 (2017). https://doi.org/10.1126/science.aai8478.

Crossref Google Scholar
[61]

D.A. Lawson, N.R. Bhakta, K. Kessenbrock, et al., Single-cell analysis reveals a stem-cell program in human metastatic breast cancer cells, Nature 526 (2015) 131–135. https://doi.org/10.1038/nature15260.

Crossref Google Scholar
[62]

T.T. Kwan, A. Bardia, L.M. Spring, et al., A digital RNA signature of circulating tumor cells predicting early therapeutic response in localized and metastatic breast cancer, Cancer Discov. 8 (2018) 1286–1299. https://doi.org/10.1158/2159-8290.CD-18-0432.

Crossref Google Scholar
[63]

Z. Su, Z. Wang, X. Ni, J. Duan, et al., Inferring the evolution and progression of small-cell lung cancer by single-cell sequencing of circulating tumor cells, Clin. Cancer Res. 25 (2019) 5049–5060. https://doi.org/10.1158/1078-0432.CCR-18-3571.

Crossref Google Scholar
[64]

N. Kanarek, B. Petrova, D.M. Sabatini, Dietary modifications for enhanced cancer therapy, Nature 579 (2020) 507–517. https://doi.org/10.1038/s41586-020-2124-0.

Crossref Google Scholar
[65]

O. Descamps, J. Riondel, V. Ducros, et al., Mitochondrial production of reactive oxygen species and incidence of age-associated lymphoma in OF1 mice: effect of alternate-day fasting, Mech. Ageing Dev. 126 (2005) 1185–1191. https://doi.org/10.1016/j.mad.2005.06.007.

Crossref Google Scholar
[66]

C. Lee, L. Raffaghello, S. Brandhorst, et al., Fasting cycles retard growth of tumors and sensitize a range of cancer cell types to chemotherapy, Sci. Transl. Med. 4 (2012) 1–7. https://doi.org/10.1126/scitranslmed.3003293.

Crossref Google Scholar
[67]

T.N. Seyfried, T.M. Sanderson, M.M. El-Abbadi, et al., Role of glucose and ketone bodies in the metabolic control of experimental brain cancer, Br. J. Cancer 89 (2003) 1375–1382. https://doi.org/10.1038/sj.bjc.6601269.

Crossref Google Scholar
[68]

K. Zaugg, Y. Yao, P.T. Reilly, et al., Carnitine palmitoyltransferase 1C promotes cell survival and tumor growth under conditions of metabolic stress, Genes Dev. 25 (2011) 1041–1051. https://doi.org/10.1101/gad.1987211.

Crossref Google Scholar
[69]

R. Camarda, A.Y. Zhou, R.A. Kohnz, et al., Inhibition of fatty acid oxidation as a therapy for MYC-overexpressing triple-negative breast cancer, Nat. Med. 22 (2016) 427–432. https://doi.org/10.1038/nm.4055.

Crossref Google Scholar
[70]

G. Pascual, D. Domínguez, M. Elosúa-Bayes, et al., Dietary palmitic acid promotes a prometastatic memory via Schwann cells, Nature 599 (2021) 485–490. https://doi.org/10.1038/s41586-021-04075-0.

Crossref Google Scholar
[71]

W. Zhao, A. Dovas, E.F. Spinazzi, et al., Deconvolution of cell type-specific drug responses in human tumor tissue with single-cell RNA-seq, Genome Med. 13 (2021) 1–15. https://doi.org/10.1186/s13073-021-00894-y.

Crossref Google Scholar
[72]

F. Tang, C. Barbacioru, Y. Wang, et al., mRNA-Seq whole-transcriptome analysis of a single cell, Nat. Methods 6 (2009) 377–382. https://doi.org/10.1038/nmeth.1315.

Crossref Google Scholar
[73]

N. Thrupp, C. Sala Frigerio, L. Wolfs, et al., Single-nucleus RNA-seq is not suitable for detection of microglial activation genes in humans, Cell Rep. 32(2020) 108189. https://doi.org/10.1016/j.celrep.2020.108189.

Crossref Google Scholar
[74]

B. Hwang, J.H. Lee, D. Bang, Single-cell RNA sequencing technologies and bioinformatics pipelines, Exp. Mol. Med. 50 (2018). https://doi.org/10.1038/s12276-018-0071-8.

Crossref Google Scholar
[75]

A.A. Kolodziejczyk, J.K. Kim, V. Svensson, et al., The technology and biology of single-cell RNA sequencing, Mol. Cell 58 (2015) 610–620. https://doi.org/10.1016/j.molcel.2015.04.005.

Crossref Google Scholar
[76]

A. Haque, J. Engel, S.A. Teichmann, et al., A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications, Genome Med. 9 (2017) 1–12. https://doi.org/10.1186/s13073-017-0467-4.

Crossref Google Scholar
[77]

D. Ramsköld, S. Luo, Y.C. Wang, et al., Full-length mRNA-Seq from singlecell levels of RNA and individual circulating tumor cells, Nat. Biotechnol. 30 (2012) 777–782. https://doi.org/10.1038/nbt.2282.

Crossref Google Scholar
[78]

S. Islam, U. Kjällquist, A. Moliner, et al., Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq, Genome Res. 21(2011) 1160–1167. https://doi.org/10.1101/gr.110882.110.

Crossref Google Scholar
[79]

T. Hashimshony, F. Wagner, N. Sher, et al., CEL-seq: Single-cell RNA-seq by multiplexed linear amplification, Cell Rep. 2 (2012) 666–673. https://doi.org/10.1016/j.celrep.2012.08.003.

Crossref Google Scholar
[80]

S. Islam, U. Kjällquist, A. Moliner, et al., Highly multiplexed and strandspecific single-cell RNA 5’ end sequencing, Nat. Protoc. 7 (2012) 813–828. https://doi.org/10.1038/nprot.2012.022.

Crossref Google Scholar
[81]

S. Islam, A. Zeisel, S. Joost, et al., Quantitative single-cell RNA-seq with unique molecular identifiers, Nat. Methods 11 (2014) 163–166. https://doi.org/10.1038/nmeth.2772.

Crossref Google Scholar
[82]

E.Z. Macosko, A. Basu, R. Satija, et al., Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets, Cell 161(2015) 1202–1214. https://doi.org/10.1016/j.cell.2015.05.002.

Crossref Google Scholar
[83]

C. Ziegenhain, B. Vieth, S. Parekh, et al., Comparative analysis of singlecell RNA sequencing methods, Mol. Cell 65 (2017) 631-643. https://doi.org/10.1016/j.molcel.2017.01.023.

Crossref Google Scholar
[84]

A. Lafzi, C. Moutinho, S. Picelli, et al., Tutorial: guidelines for the experimental design of single-cell RNA sequencing studies, Nat. Protoc. 13(2018) 2742–2757. https://doi.org/10.1038/s41596-018-0073-y.

Crossref Google Scholar
[85]

H. Nguyen, D. Tran, B. Tran, et al., A comprehensive survey of regulatory network inference methods using single cell RNA sequencing data, Brief. Bioinform. 22 (2021) 1–15. https://doi.org/10.1093/bib/bbaa190.

Crossref Google Scholar
[86]

A. Dal Molin, B. Di Camillo, How to design a single-cell RNA-sequencing experiment: Pitfalls, challenges and perspectives, Brief. Bioinform. 20 (2018) 1384–1394. https://doi.org/10.1093/bib/bby007.

Crossref Google Scholar
[87]

L.E. Mickelsen, M. Bolisetty, B.R. Chimileski, et al., Single-cell transcriptomic analysis of the lateral hypothalamic area reveals molecularly distinct populations of inhibitory and excitatory neurons, Nat. Neurosci. 22(2019) 642–656. https://doi.org/10.1038/s41593-019-0349-8.

Crossref Google Scholar
[88]

E. Armingol, A. Officer, O. Harismendy, et al., Deciphering cell–cell interactions and communication from gene expression, Nat. Rev. Genet. 22(2021) 71–88. https://doi.org/10.1038/s41576-020-00292-x.

Crossref Google Scholar
[89]

N. Salehi, M.H. Karimi-Jafari, M. Totonchi, et al., Integration and gene co-expression network analysis of scRNA-seq transcriptomes reveal heterogeneity and key functional genes in human spermatogenesis, Sci. Rep. 11 (2021) 1–13. https://doi.org/10.1038/s41598-021-98267-3.

Crossref Google Scholar
[90]

A. Bhattacherjee, M.N. Djekidel, R. Chen, et al., Cell type-specific transcriptional programs in mouse prefrontal cortex during adolescence and addiction, Nat. Commun. 10 (2019) 1–18. https://doi.org/10.1038/s41467-019-12054-3.

Crossref Google Scholar
[91]

R. Chen, X. Wu, L. Jiang, et al., Single-cell RNA-Seq reveals hypothalamic cell diversity, Cell Rep. 18 (2017) 3227–3241. https://doi.org/10.1016/j.celrep.2017.03.004.

Crossref Google Scholar
[92]

H. Choi, Y. Choi, E.J. Lee, et al., Hippocampal glucose uptake as a surrogate of metabolic change of microglia in Alzheimer’s disease, J. Neuroinflammation 18 (2021) 190. https://doi.org/10.1186/s12974-021-02244-6.

Crossref Google Scholar
[93]

C.J. Finno, J. Peterson, M. Kang, et al., Single-cell RNA-seq reveals profound alterations in mechanosensitive dorsal root ganglion neurons with vitamin e deficiency, IScience 21 (2019) 720–735. https://doi.org/10.1016/j.isci.2019.10.064.

Crossref Google Scholar
[94]

T.R. Hammond, C. Dufort, L. Dissing-Olesen, et al., Single-cell RNA sequencing of microglia throughout the mouse lifespan and in the injured brain reveals complex cell-state changes, Immunity 50 (2019) 253-271. https://doi.org/10.1016/j.immuni.2018.11.004.

Crossref Google Scholar
[95]

B. Tepe, M.C. Hill, B.T. Pekarek, et al., Single-cell RNA-seq of mouse olfactory bulb reveals cellular heterogeneity and activity-dependent molecular census of adult-born neurons, Cell Rep. 25 (2018) 2689-2703. https://doi.org/10.1016/j.celrep.2018.11.034.

Crossref Google Scholar
[96]

J. Kupari, M. Häring, E. Agirre, et al., An atlas of vagal sensory neurons and their molecular specialization, Cell Rep. 27 (2019) 2508-2523. https://doi.org/10.1016/j.celrep.2019.04.096.

Crossref Google Scholar
[97]

Q. Li, Z. Cheng, L. Zhou, et al., Developmental heterogeneity of microglia and brain myeloid cells revealed by deep Single-cell RNA sequencing, Neuron 101 (2019) 207-223. https://doi.org/10.1016/j.neuron.2018.12.006.

Crossref Google Scholar
[98]

N. Ochocka, P. Segit, K.A. Walentynowicz, et al., Single-cell RNA sequencing reveals functional heterogeneity of glioma-associated brain macrophages, Nat. Commun. 12 (2021) 1–14. https://doi.org/10.1038/s41467-021-21407-w.

Crossref Google Scholar
[99]

J. Peng, A.L. Sheng, Q. Xiao, et al., Single-cell transcriptomes reveal molecular specializations of neuronal cell types in the developing cerebellum, J. Mol. Cell Biol. 11 (2019) 636–648. https://doi.org/10.1093/jmcb/mjy089.

Crossref Google Scholar
[100]

E.M. Rueda, B.M. Hall, M.C. Hill, et al., The hippo pathway blocks mammalian retinal müller glial cell reprogramming, Cell Rep. 27 (2019) 1637-1649. https://doi.org/10.1016/j.celrep.2019.04.047.

Crossref Google Scholar
[101]

L. Todd, I. Palazzo, L. Suarez, et al., Reactive microglia and IL1β/IL-1R1-signaling mediate neuroprotection in excitotoxin-damaged mouse retina, J. Neuroinflammation 16 (2019) 1–19. https://doi.org/10.1186/s12974-019-1505-5.

Crossref Google Scholar
[102]

H. Van Hove, L. Martens, I. Scheyltjens, et al., A single-cell atlas of mouse brain macrophages reveals unique transcriptional identities shaped by ontogeny and tissue environment, Nat. Neurosci. 22 (2019) 1021–1035. https://doi.org/10.1038/s41593-019-0393-4.

Crossref Google Scholar
[103]

J.C. Wester, V. Mahadevan, C.T. Rhodes, et al., Neocortical projection neurons instruct inhibitory interneuron circuit development in a lineagedependent manner, Neuron 102 (2019) 960-975. https://doi.org/10.1016/j.neuron.2019.03.036.

Crossref Google Scholar
[104]

G. Xiong, B. Zhang, B. Song, et al., Single-cell RNA sequencing reveals adverse effects of paraquat on the fate commitment of murine neural stem cells, Sci. Total Environ. 785 (2021) 147386. https://doi.org/10.1016/j.scitotenv.2021.147386.

Crossref Google Scholar
[105]

M.T. Dang, F. Mafra, M. Haldar, Isolation of myeloid cells from mouse brain tumors for single-cell RNA-seq analysis, STAR Protoc. 2 (2021) 100957. https://doi.org/10.1016/j.xpro.2021.100957.

Crossref Google Scholar
[106]

H.S. Yang, K.D. Onos, K. Choi, et al., Natural genetic variation determines microglia heterogeneity in wild-derived mouse models of Alzheimer’s disease, Cell Rep. 34 (2021) 108739. https://doi.org/10.1016/j.celrep.2021.108739.

Crossref Google Scholar
[107]

C.B. Avalos, R. Brugmann, S.G. Sprecher, Single cell transcriptome atlas of the drosophila larval brain, Elife. 8 (2019) 1–25. https://doi.org/10.7554/eLife.50354.

Crossref Google Scholar
[108]

T.H. Nguyen, R. Vicidomini, S.D. Choudhury, et al., Single-cell RNA sequencing analysis of the drosophila larval ventral cord, Curr. Protoc. 1(2021) 1–27. https://doi.org/10.1002/cpz1.38.

Crossref Google Scholar
[109]

S. Yu, J. He, Stochastic cell-cycle entry and cell-state-dependent fate outputs of injury-reactivated tectal radial glia in zebrafish, Elife. 8 (2019) 1–27. https://doi.org/10.7554/eLife.48660.

Crossref Google Scholar
[110]

Z.Y. Wang, A. Keogh, A. Waldt, et al., Single-cell and bulk transcriptomics of the liver reveals potential targets of NASH with fibrosis, Sci. Rep. 11(2021) 1–15. https://doi.org/10.1038/s41598-021-98806-y.

Crossref Google Scholar
[111]

T. Kimura, Y. Kato, Y. Ozawa, et al., Immunomodulatory activity of lenvatinib contributes to antitumor activity in the Hepa1-6 hepatocellular carcinoma model, Cancer Sci. 109 (2018) 3993–4002. https://doi.org/10.1111/cas.13806.

Crossref Google Scholar
[112]

O. Krenkel, J. Hundertmark, A.T. Abdallah, et al., Myeloid cells in liver and bone marrow acquire a functionally distinct inflammatory phenotype during obesity-related steatohepatitis, Gut 69 (2020) 551–563. https://doi.org/10.1136/gutjnl-2019-318382.

Crossref Google Scholar
[113]

L. Planas-Paz, T. Sun, M. Pikiolek, et al., Signaling in biliary epithelial cells promotes a ductular reaction in response to liver injury, Cell Stem Cell 25(2019) 39-53. e10. https://doi.org/10.1016/j.stem.2019.04.005.

Crossref Google Scholar
[114]

Q. Su, S.Y. Kim, F. Adewale, et al., Single-cell RNA transcriptome landscape of hepatocytes and non-parenchymal cells in healthy and NAFLD mouse liver, IScience 24 (2021). https://doi.org/10.1016/j.isci.2021.103233.

Crossref Google Scholar
[115]

X. Xiong, H. Kuang, S. Ansari, et al., Landscape of intercellular crosstalk in healthy and NASH liver revealed by single-cell secretome gene analysis, Mol. Cell 75 (2019) 644-660. https://doi.org/10.1016/j.molcel.2019.07.028.

Crossref Google Scholar
[116]

W. Yang, H. He, T. Wang, et al., Single-cell transcriptomic analysis reveals a hepatic stellate cell–activation roadmap and myofibroblast origin during liver fibrosis in mice, Hepatology 74 (2021) 2774–2790. https://doi.org/10.1002/hep.31987.

Crossref Google Scholar
[117]

A. Arazi, D.A. Rao, C.C. Berthier, et al., The immune cell landscape in kidneys of patients with lupus nephritis, Nat. Immunol. 20 (2019) 902–914. https://doi.org/10.1038/s41590-019-0398-x.

Crossref Google Scholar
[118]

B. Zhang, H. Li, L. Zhu, X. He, et al., Single-cell transcriptomics uncovers potential marker genes of ochratoxin A–sensitive renal cells in an acute toxicity rat model, Cell Biol. Toxicol. 37 (2021) 7–13. https://doi.org/10.1007/s10565-020-09531-7.

Crossref Google Scholar
[119]

K. Parikh, A. Antanaviciute, D. Fawkner-Corbett, et al., Colonic epithelial cell diversity in health and inflammatory bowel disease, Nature 567 (2019) 49–55. https://doi.org/10.1038/s41586-019-0992-y.

Crossref Google Scholar
[120]

A. Aliluev, S. Tritschler, M. Sterr, et al., Diet-induced alteration of intestinal stem cell function underlies obesity and prediabetes in mice, Nat. Metab. 3(2021) 1202–1216. https://doi.org/10.1038/s42255-021-00458-9.

Crossref Google Scholar
[121]

C.W. Cheng, M. Biton, A.L. Haber, et al., Ketone body signaling mediates intestinal stem cell homeostasis and adaptation to diet, Cell 178 (2019) 1115-1131. https://doi.org/10.1016/j.cell.2019.07.048.

Crossref Google Scholar
[122]

M.D. Mana, A.M. Hussey, C.N. Tzouanas, et al., High-fat diet-activated fatty acid oxidation mediates intestinal stemness and tumorigenicity, Cell Rep. 35 (2021) 109212. https://doi.org/10.1016/j.celrep.2021.109212.

Crossref Google Scholar
[123]

J. Yu, L. Chen, W. Gu, et al., Heterogeneity effects of nanoplastics and lead on zebrafish intestinal cells identified by single-cell sequencing, Chemosphere 289 (2022) 133133. https://doi.org/10.1016/j.chemosphere.2021.133133.

Crossref Google Scholar
[124]

N. Bossel Ben-Moshe, S. Hen-Avivi, N. Levitin, et al., Predicting bacterial infection outcomes using single cell RNA-sequencing analysis of human immune cells, Nat. Commun. 10 (2019) 1–16. https://doi.org/10.1038/s41467-019-11257-y.

Crossref Google Scholar
[125]

M. Cella, R. Gamini, C. Sécca, et al., Subsets of ILC3-ILC1-like cells generate a diversity spectrum of innate lymphoid cells in human mucosal tissues, Nat. Immunol. 20 (2019) 980–991. https://doi.org/10.1038/s41590-019-0425-y.

Crossref Google Scholar
[126]

M. Gutierrez-Arcelus, N. Teslovich, A.R. Mola, et al., Lymphocyte innateness defined by transcriptional states reflects a balance between proliferation and effector functions, Nat. Commun. 10 (2019). https://doi.org/10.1038/s41467-019-08604-4.

Crossref Google Scholar
[127]

G. Pizzolato, H. Kaminski, M. Tosolini, et al., Single-cell RNA sequencing unveils the shared and the distinct cytotoxic hallmarks of human TCRVδ1 and TCRVδ2 γδ T lymphocytes, Proc. Natl. Acad. Sci. U.S.A. 116 (2019) 11906–11915. https://doi.org/10.1073/pnas.1818488116.

Crossref Google Scholar
[128]

A. Takeda, M. Hollmén, D. Dermadi, et al., Single-cell survey of human lymphatics unveils marked endothelial cell heterogeneity and mechanisms of homing for neutrophils, Immunity 51 (2019) 561-572. https://doi.org/10.1016/j.immuni.2019.06.027.

Crossref Google Scholar
[129]

M. Balzano, M. De Grandis, T.P. Vu Manh, et al., Nidogen-1 contributes to the interaction network involved in pro-B cell retention in the peri-sinusoidal hematopoietic stem cell niche, Cell Rep. 26 (2019) 3257-3271. https://doi.org/10.1016/j.celrep.2019.02.065.

Crossref Google Scholar
[130]

A.P. Croft, J. Campos, K. Jansen, et al., Distinct fibroblast subsets drive inflammation and damage in arthritis, Nature 570 (2019) 246–251. https://doi.org/10.1038/s41586-019-1263-7.

Crossref Google Scholar
[131]

S. Culemann, A. Grüneboom, J.Á. Nicolás-Ávila, et al., Locally renewing resident synovial macrophages provide a protective barrier for the joint, Nature 572 (2019) 670–675. https://doi.org/10.1038/s41586-019-1471-1.

Crossref Google Scholar
[132]

M.W. Dahlgren, S.W. Jones, K.M. Cautivo, et al., Adventitial stromal cells define group 2 innate lymphoid cell tissue niches, Immunity 50 (2019) 707-722. https://doi.org/10.1016/j.immuni.2019.02.002.

Crossref Google Scholar
[133]

U. Gowthaman, J.S. Chen, B. Zhang, et al., Identification of a T follicular helper cell subset that drives anaphylactic IgE, Science 365 (2019). https://doi.org/10.1126/science.aaw6433.

Crossref Google Scholar
[134]

C. Harly, D. Kenney, G. Ren, et al., The transcription factor TCF-1 enforces commitment to the innate lymphoid cell lineage, Nat. Immunol. 20 (2019) 1150–1160. https://doi.org/10.1038/s41590-019-0445-7.

Crossref Google Scholar
[135]

S. Hemmers, M. Schizas, E. Azizi, et al., IL-2 production by self-reactive CD4 thymocytes scales regulatory T cell generation in the thymus, J. Exp. Med. 216 (2019) 2466–2478. https://doi.org/10.1084/jem.20190993.

Crossref Google Scholar
[136]

C. Liu, M. Chikina, R. Deshpande, et al., Treg cells promote the SREBP1-dependent metabolic fitness of tumorpromoting macrophages via repression of CD8+ T cell-derived interferon-γ, Immunity 51 (2019) 381-397. e6. https://doi.org/10.1016/j.immuni.2019.06.017.

Crossref Google Scholar
[137]

P. Lo, Y. Yao, Q. Zhou, Single-cell RNA-seq reveals obesity-induced alterations in the brca1-mutated mammary gland microenvironment, Cancers (Basel). 12 (2020) 2235. https://doi.org/10.3390/cancers12082235.

Crossref Google Scholar
[138]

H. Nagashima, T. Mahlakõiv, H. Shih, et al., Neuropeptide CGRP limits group 2 innate lymphoid cell responses and constrains type 2 inflammation, Immunity 51 (2019) 682-695. https://doi.org/10.1016/j.immuni.2019.06.009.

Crossref Google Scholar
[139]

J. Schulthess, S. Pandey, M. Capitani, et al., The short chain fatty acid butyrate imprints an antimicrobial program in macrophages, Immunity 50(2019) 432-445. e7. https://doi.org/10.1016/j.immuni.2018.12.018.

Crossref Google Scholar
[140]

C. Yao, H.W. Sun, N.E. Lacey, et al., Single-cell RNA-seq reveals TOX as a key regulator of CD8+ T cell persistence in chronic infection, Nat. Immunol. 20 (2019) 890–901. https://doi.org/10.1038/s41590-019-0403-4.

Crossref Google Scholar
[141]

N. Baryawno, D. Przybylski, M.S. Kowalczyk, et al., A cellular taxonomy of the bone marrow stroma in homeostasis and leukemia, Cell 177 (2019) 1915-1932. e16. https://doi.org/10.1016/j.cell.2019.04.040.

Crossref Google Scholar
[142]

S. Ma, S. Sun, L. Geng, et al., Caloric restriction reprograms the single-cell transcriptional landscape of rattus norvegicus aging, Cell 180 (2020) 984-1001. https://doi.org/10.1016/j.cell.2020.02.008.

Crossref Google Scholar
[143]

C. Neftel, J. Laffy, M.G. Filbin, et al., An integrative model of cellular states, plasticity, and genetics for glioblastoma, Cell 178 (2019) 835-849. https://doi.org/10.1016/j.cell.2019.06.024.

Crossref Google Scholar
[144]

L. Wang, S. Mo, X. Li, et al., Single-cell RNA-seq reveals the immune escape and drug resistance mechanisms of mantle cell lymphoma, Cancer Biol. Med. 17 (2020) 726–739. https://doi.org/10.20892/j.issn.2095-3941.2020.0073.

Crossref Google Scholar
[145]

C. Ye, Q. Zhou, Y. Hong, et al., Role of alternative polyadenylation dynamics in acute myeloid leukaemia at single-cell resolution, RNA Biol. 16(2019) 785–797. https://doi.org/10.1080/15476286.2019.1586139.

Crossref Google Scholar
[146]

P. Zhang, M. Yang, Y. Zhang, et al., Dissecting the single-cell transcriptome network underlying gastric premalignant lesions and early gastric cancer, Cell Rep. 27 (2019) 1934-1947. https://doi.org/10.1016/j.celrep.2019.04.052.

Crossref Google Scholar
[147]

S. Kurtulus, A. Madi, G. Escobar, et al., Checkpoint blockade immunotherapy induces dynamic changes in PD-1 - CD8+ tumorinfiltrating T cells, Immunity 50 (2019) 181-194. https://doi.org/10.1016/j.immuni.2018.11.014.

Crossref Google Scholar
[148]

N.K. Lytle, L.P. Ferguson, N. Rajbhandari, et al., A multiscale map of the stem cell state in pancreatic adenocarcinoma, Cell 177 (2019) 572-586. https://doi.org/10.1016/j.cell.2019.03.010.

Crossref Google Scholar
[149]

M.C. Vladoiu, I. El-Hamamy, L.K. Donovan, et al., Childhood cerebellar tumours mirror conserved fetal transcriptional programs, Nature 572 (2019) 67–73. https://doi.org/10.1038/s41586-019-1158-7.

Crossref Google Scholar
[150]

J.L. Benci, L.R. Johnson, R. Choa, et al., Opposing functions of interferon coordinate adaptive and innate immune responses to cancer immune checkpoint blockade, Cell 178 (2019) 933-948. https://doi.org/10.1016/j.cell.2019.07.019.

Crossref Google Scholar
[151]

S.P. Arlauckas, S.B. Garren, C.S. Garris, et al., Arg1 expression defines immunosuppressive subsets of tumor-associated macrophages, Theranostics 8 (2018) 5842–5854. https://doi.org/10.7150/thno.26888.

Crossref Google Scholar
[152]

H.J. Chen, A. Poran, A.M. Unni, et al., Generation of pulmonary neuroendocrine cells and SCLC-like tumors from human embryonic stem cells, J. Exp. Med. 216 (2019) 674–687. https://doi.org/10.1084/jem.20181155.

Crossref Google Scholar
[153]

S.P. Kubli, L. Vornholz, G. Duncan, et al., Fcmr regulates mononuclear phagocyte control of anti-tumor immunity, Nat. Commun. 10 (2019). https://doi.org/10.1038/s41467-019-10619-w.

Crossref Google Scholar
[154]

Y. Li, X. Ma, Z. Chen, H. Wu, et al., B1 oligomerization regulates PML nuclear body biogenesis and leukemogenesis, Nat. Commun. 10 (2019). https://doi.org/10.1038/s41467-019-11746-0.

Crossref Google Scholar
[155]

D.N. Debruyne, R. Dries, S. Sengupta, et al., BORIS promotes chromatin regulatory interactions in treatment-resistant cancer cells, Nature 572 (2019) 676–680. https://doi.org/10.1038/s41586-019-1472-0.

Crossref Google Scholar
[156]

M.B. Dong, G. Wang, R.D. Chow, et al., Systematic immunotherapy target discovery using genome-scale in vivo CRISPR screens in CD8 T cells, Cell 178 (2019) 1189-1204. https://doi.org/10.1016/j.cell.2019.07.044.

Crossref Google Scholar
[157]

K. Grosselin, A. Durand, J. Marsolier, et al., High-throughput single-cell ChIP-seq identifies heterogeneity of chromatin states in breast cancer, Nat. Genet. 51 (2019) 1060–1066. https://doi.org/10.1038/s41588-019-0424-9.

Crossref Google Scholar
[158]

V. Hovestadt, K.S. Smith, L. Bihannic, et al., Resolving medulloblastoma cellular architecture by single-cell genomics, Nature 572 (2019) 74–79. https://doi.org/10.1038/s41586-019-1434-6.

Crossref Google Scholar
[159]

H. Liu, J. Golji, L.K. Brodeur, et al., Tumor-derived IFN triggers chronic pathway agonism and sensitivity to ADAR loss, Nat. Med. 25 (2019) 95–102. https://doi.org/10.1038/s41591-018-0302-5.

Crossref Google Scholar
[160]

C.C. Bell, K.A. Fennell, Y.C. Chan, et al., Targeting enhancer switching overcomes non-genetic drug resistance in acute myeloid leukaemia, Nat. Commun. 10 (2019) 1–15. https://doi.org/10.1038/s41467-019-10652-9.

Crossref Google Scholar
[161]

J.V. McCann, L. Xiao, D.J. Kim, et al., Endothelial miR-30c suppresses tumor growth via inhibition of TGF-β-induced Serpine1, J. Clin. Invest. 129(2019) 1654–1670. https://doi.org/10.1172/JCI123106.

Crossref Google Scholar
[162]

D. Merino, T.S. Weber, A. Serrano, et al., Barcoding reveals complex clonal behavior in patient-derived xenografts of metastatic triple negative breast cancer, Nat. Commun. 10 (2019). https://doi.org/10.1038/s41467-019-08595-2.

Crossref Google Scholar
[163]

R.M. Zemek, E. De Jong, W.L. Chin, et al., Sensitization to immune checkpoint blockade through activation of a STAT1/NK axis in the tumor microenvironment, Sci. Transl. Med. 11 (2019). https://doi.org/10.1126/scitranslmed.aav7816.

Crossref Google Scholar
[164]

G. Liu, Y. Li, T. Zhang, et al., Single-cell RNA sequencing reveals sexually dimorphic transcriptome and type 2 diabetes genes in mouse islet β cells, Genomics. Proteomics Bioinformatics 19 (2021) 408–422. https://doi.org/10.1016/j.gpb.2021.07.004.

Crossref Google Scholar
Food Science and Human Wellness
Volume 13 Issue 1,
January 2024
Pages 75-89
DOI: 10.26599/FSHW.2022.9250006
Cite this article:
Wang X, Cheng X, Liu H, et al. Food nutrition and toxicology targeting on specific organs in the era ofsingle-cell sequencing. Food Science and Human Wellness, 2024, 13(1): 75-89. https://doi.org/10.26599/FSHW.2022.9250006
Metrics & Citations  
Article History
Copyright
Rights and Permissions
Return

Brief overview of scRNA-seq in different tissues.

Tissue/cell scRNA-seq Research achievement Reference
Brain
Review Brain Summarize progress and challenges in applying scRNA-seq to advance the understanding of the brain[29]
Human Prefrontal cortex 10× GenomicsIdentify highly cell-type specific during early Alzheimer’s disease pathophysiology[36]
Schwann cells FACSDietary palmitic acid induces a stable prometastatic memory in human oral squamous cell carcinoma cells, tumour-associated Schwann cells and lymphatic endothelial cells displayed the highest proportional changes[70]
Neural stem cells BD Rhapsody systemCadmium exposure led to an increase in the differentiation of neural stem cells into astrocytes while a decrease into neurons, and induced subtype-specific response and dysregulated cell-to-cell communication[28]
Hippocampal FACSFound a layer Ⅰ interneuron expressing Pax6 and a distinct postmitotic oligodendrocyte subclass marked by Itpr2, transcription factors formed a complex, layered regulatory code across the diversity of cortical cell types[30]
Gliomas FACSCharacterize H3K27M-glioma primarily contain cells resemble oligodendrocyte precursor cells with greater proliferation and tumor-propagating[58]
Lateral hypothalamic area 10× GenomicsDefine 15 distinct populations of glutamatergic neurons and 15 of GABAergic neurons, in the mouse lateral hypothalamic area[87]
Prefrontal cortex 10× GenomicsClassify unique subtypes in the prefrontal cortex, and analyze transcriptional dynamics of each cell subtype[90]
Hypothalamus Drop-seqDefine 11 distinct non-neuronal and 34 neuronal cell clusters, each with differential transcriptional effects under food deprivation[91]
Microglia 10× GenomicsIdentify reconfiguration of microglial glucose metabolism, including glucose transporters, glycolysis and oxidative phosphorylation, in hippocampus of Alzheimer’s disease[92]
Ganglion neurons 10× GenomicsVitamin E deficiency resulted in profound alterations in tyrosine-hydroxylase positive dorsal root ganglion neurons, while its supplementation improved mechanosensation[93]
Microglia 10× GenomicsUncover at least nice transcriptionally distinct microglial states, most diverse during early development and less heterogenous in adulthood until perturbed by injury or aging[94]
Mice Olfactory bulb 10× GenomicsInvestigate the subtype-specific neuronal functions, and characterizing the molecular profiles during the maturation and integration of adult-born neurons[95]
Sensory neuron 10× GenomicsReveal differences between neurons derived from different embryonic origins, and a vast diversity of vagal neuron types with previously unanticipated and proposed types[96]
Microglia and myeloid cells Smart-seq2Discover the heterogeneous status in early postnatal microglia, with a proliferative-region-associated subset similar to degenerative disease-associated microglia[97]
Myeloid in gliomas 10× GenomicsDemonstrate molecular heterogeneity among myeloid cells in naïve and glioma- bearing mice brains, distinct spatial distribution of identified subsets in gliomas[98]
Cerebellum 10x GenomicsIdentify eight cell clusters in postnatal mouse cerebellum, revealing trajectory hierarchies of granule cells[99]
Glial cells 10× GenomicsIdentify a Hippo non-responsive form of YAP may drive the Müller glial cells to a proliferative, progenitor-like state[100]
Retinal neurons and glia 10× GenomicsN-methyl-D-aspartate induced upregulation of pro-inflammatory cytokines, and genes associated with IL1β-signaling in different types of retinal neurons and glia[101]
Brain 10× GenomicsUnravel immune cell heterogeneity in the brain, and non-parenchymal macrophages were more heterogeneous[102]
Brain 10× GenomicsMajor subtypes of caudal ganglionic eminence induced inhibitory interneurons were conserved after loss of intratelencephalic-type projection neurons, but with differential synaptic proteins and signaling molecules.[103]
Neural stem cell BD Rhapsody systemThe agricultural herbicide paraquat exposure altered differentiation the proportions of neural stem cells, with decreased neurons and increased astrocytes, and ROS played a key role in paraquat-altered neuronal reduction[104]
Cerebellum 10× ChromiumA protocol to minimize cell damage for scRNA-seq[105]
Brain myeloid cell and microglia 10× GenomicsGenetic diversity led to variation in the abundance of microglial subtypes (disease-associated and interferon-responding microglia) with response to amyloidosis[106]
Drosophila Larval brain lobes 10× GenomicsDistinguish five major groups: neural progenitors, differentiated neurons, glia, undifferentiated neurons and non-neural cells[107]
Larval ventral nerve cord 10× GenomicsDescribe a set of protocols optimized for scRNA-seq analysis of the Drosophila larval ventral nerve cord, from tissue dissection and cell dissociation to cDNA library preparation, sequencing, and data analysis[108]
Zebrafish Optic tecta 10× GenomicsNotch/Delta lateral inhibition was involved in the stochastic cell-cycle entry driven by the injury- reactivated tectal radial glia[109]
Liver
Review Liver Discuss scRNA application in fundamental liver biology such as the metabolic zonation of hepatocytes, endothelial cells and hepatic stellate cells, and the cellular mechanisms underpinning liver regeneration[40]
Human Liver tissue 10× GenomicsHighlight a clear cell state transition from RGS5-expressing hepatic stellate cells to myofibroblast-like fibrogenic activated hepatic stellate cells that express extracellular matrix proteins and profibrotic mediators[110]
Liver FACS Obtain the zonation profiles of all liver genes with single-molecule fluorescence in situ hybridization, with around 50% of liver genes significantly zonated [39]
Hepatocytes Drop-seq Characterize how the transcriptomic landscape of individual hepatocytes was altered in response to high-fat diet and nutritional overload in NAFLD [41]
Hepa1-6 cell 10× Genomics Investigated the effects of lenvatinib (a multiple receptor tyrosine kinase inhibitor) alone and in combination with anti-PD-1 on the immune cell populations in tumors [111]a
Myeloid cells in liver and bone marrow FACS; 10x Genomics Assess the heterogeneity of myeloid cells in the liver and bone marrow during NAFLD, with upregulation of monocyte-derived populations, and downregulation of inflammatory calprotectin in macrophage and dendritic cell subsets [112]
Mice Biliary epithelial cells 10× Genomics Indicate the absence of WNT/b-catenin signaling, whereas the activation of YAP signaling in biliary epithelial cells during the ductular reaction to support liver regeneration following injury [113]
Hepatocytes and non-parenchymal cells 10× Genomics Reveal the gene expression landscape of hepatocytes and non-parenchymal cells in healthy, NASH and NAFLD livers, with Kupffer cells acting immune responses and monocyte-derived macrophages capable of differentiation [114]
Non-parenchymal cells from livers 10× Genomics Uncover the emergence of NASH-associated macrophages with high expression of triggering receptors expressed on myeloid cells 2, highly responsive to dietary interventions [115]
Hepatic stellate and fibroblasts 10× Genomics Provide a transcriptional roadmap during liver fibrosis for the activation of hepatic stellate cells with the sequential activation of inflammatory, migrative, and extracellular matrix– producing programs [116]
Kidney
Human Kidney cells Smart-seq2 Reveal 21 subsets of leukocytes active in lupus nephritis, including myeloid cells, T cells, natural killer cells and B cells with both pro-inflammatory responses and inflammation-resolving responses [117]
Mice Kidney cells 10× Genomics The protective effect of Lactobacillus casei Zhang is dependent on immune-regulatory macrophages and renal tubular epithelial cells [45]
Rat Kidney cells Reveal the heterogeneity of kidney cells in response to ochratoxin A acute toxicity, with the tubule epithelial cells as main target types [118]
Intestine
Human Epithelial cells from the colon 10× Genomics SMART-Seq2 Reveal 51 epithelial, stromal, and immune cell subsets, confirm that inflammatory fibroblasts, inflammatory monocytes, microfold-like cells, and T cells expand with disease, and the expression of an inflammation-associated gene set increased from healthy to non-inflamed to inflamed patients[54]
Epithelial cells from the colon 10× Genomics Smart-seq2 Identify previously unknown cellular subtypes gradients of progenitor cells, colonocytes and goblet cells, and absorptive cell expressing the proton channel OTOP2 and the satiety peptide uroguanylin[119]
Crypt cells from small intestinal 10× Genomics Suggest that an obesogenic western-style high-fat/high-sugar diet boosts formation of the absorptive and goblet cell lineage and promotes proximal cell identities[120]
Mice Crypt cells from intestinal 10× Genomics Indicate that 3-hydroxy-3-methylglutaryl- CoA synthetase 2, the gene encoding the rate-limiting enzyme in the production of ketone bodies, loss compromises stemness and skews their differentiation toward the secretory lineage with the progressive loss of ISCs and the shift toward Paneth and goblet cell differentiation[121]
Crypt cells from the small intestine and the colon Drop-seq Investigate the necessity of peroxisome proliferator-activated receptor and the downstream fatty acid oxidation metabolic program as drivers of intestinal stem cell adaptation to a high fat diet for the genesis and progression of early intestinal adenomas[122]
Zebrafish Epithelial cells 10× Genomics Identify seven cell populations, including lymphocytes (B cells and T cells), phagocytes (macrophages and neutrophils), enterocytes, and secretory cells (goblet cells and enteroendocrine cells), and co-exposure of polystyrene nanoplastics and lead induced lower differentially expressed genes in most cell populations, except for goblet cells[123]
Immune
Review Immune cellSummarize how scRNA-seq can be used to deconvolve immune system heterogeneity by identifying novel distinct immune cell subsets in health and disease, characterizing stochastic heterogeneity within a cell population and building developmental ‘trajectories’ for immune cells[53]
Immune cells from bronchoalveolar 10× Genomics Proinflammatory monocyte-derived macrophages and the presence of highly clonally CD8+ T cells were abundant from severe COVID-9 patients, suggesting the potentially underlying pathogenesis mechanisms[55]
Peripheral blood cells 10× Genomics Reveal cell-type specific immune responses associated not only with ex vivo infection phenotype but also with clinical disease stage[124]
Human Lymphoid cells from tonsil/intestine 10× Genomics Identify an intermediate ILC3-ILC1 cluster in the tonsils and in the lamina propria of the ileum since ILC3s acquired transcription factors and cytokines of ILC1- like cells[125]
Innate T cells from lymphocytes 10× Genomics Reveals four broad states of innateness and heterogeneity, while innateness is characterized by pre-formed mRNA encoding effector functions, but impaired proliferation with decreased expression of ribosomal genes[126]
T lymphocytes from peripheral blood 10× Genomics Unveil the shared and the distinct cytotoxic hallmarks of T cell receptors (TCRVδ and TCRVδ2) human γδ T lymphocytes [127]
Endothelial cells from lymph nodes 10× Genomics Identify six types of lymphatic endothelial cells including subcapsular and medullary sinuses, and CD209 on medullary sinuses mediates neutrophil adhesion[128]
CD4 T cells from spleen 10× Genomics Indicate the landscape of CD4 T cell subsets differs markedly, exhausted, cytotoxic, and activated regulatory T cells — in young mice but gradually accumulate with age [51]
Stromal cells from bone marrow 10× Genomics Peri-sinusoidal stromal cells were found to co-express with identified reporter genes in bone marrow stromal cell, involving in the development of hematopoietic subsets [129]
Synovial cells from ankle and wrist joint 10× Genomics Identify two distinct fibroblast subsets: FAPα+ THY1+ immune effector fibroblasts located in the synovial sub-lining, and FAPα+ THY1- destructive fibroblasts restricted to the synovial lining layer [130]
Macrophages from joints 10× Genomics Identify dynamic epithelial-like structures with CX3CR1+ tissue-resident macrophages, restricting the inflammatory reaction under a tight-junction-mediated shield [131]
Lymphoid cells from lung/adipose 10× Genomics Confirm adventitial stromal cells as a fibroblast-like subset enriched for pathways involved in extracellular matrix remodeling, immune sensing and regulation [132]
CD4+ from lymph nodes and spleen 10× Chromium Identify a T follicular helper cell subset which drives anaphylactic immunoglobulin E, with an unusual cytokine profile and co-express the transcription factors [133]
Mice Lymphoid cells from bone marrow 10× Genomics Identify two successive stages of innate lymphoid cells development-specified and committed early innate lymphoid progenitors, and the former can generate dendritic cells, whereas the later was greatly decreased [134]
Regulatory T cells from thymus 10× Genomics Interleukin-2 production by self-reactive CD4 thymocytes scales regulatory T cell generation in the thymus [135]
CD45+ cells from B16 tumors 10× Genomics Treg cells shaped the transcriptional landscape across multiple tumor-infiltrating immune cell types, promote the SREBP1-dependent metabolic fitness of tumor-promoting macrophages via repression of CD8+ T cell-derived interferon-γ [136]
Stromal and immune cells from the mammary gland 10× Genomics HFD-induced obesity establishes a tumorigenic microenvironment to promote breast cancer development through the upregulation of extracellular matrix and the downregulation of immunoregulatory ecosystem [137]
Innate lymphoid cells from lung lymphocytes 10× Genomics Multiple subsets of innate and adaptive lymphocytes emerged with different kinetics, and calcitonin gene related peptide as a context-dependent negative regulatory factor limits group 2 innate lymphoid cell responses and constrains type 2 inflammation [138]
Macrophages from colon and bone marrow 10× Genomics Identify butyrate-induced antimicrobial peptides, associated with a shift in macrophage metabolism through histone deacetylase 3 inhibition [139]
CD8+ T cells during virus infections 10× Genomics Long-term CD8+ T cell immunity to chronic viral infection requires unique transcriptional and epigenetic programs associated with the transcription factor TOX. [140]
Stroma cells from bone marrow 10x Genomics Identify seventeen stromal subsets expressing distinct hematopoietic regulatory genes spanning new fibroblastic and osteoblastic subpopulations including distinct osteoblast differentiation trajectories [141]
Rat Cells from adipose, aorta, kidney, liver, skin, bone marrow 10× Genomics Caloric restriction reprograms the single-cell transcriptional landscape of norvegicus aging, alleviating aging-related accumulation of pro-inflammatory cells and attenuating aging-associated cell-type-specific gene expression changes [142]
Tumor
Pancreatic tissue ddSEQ System Reveal epithelial and stromal heterogeneous alterations within the tumor microenvironment during the progression of cancer, progressively depletion of cytotoxic T cells, activated T-helper cells, and dendritic cells [57]
Glioblastoma cells from brain SMART-Seq2 10× Genomics Find that malignant cells in glioblastoma exist in a limited set of cellular states that recapitulate neural progenitor, oligodendrocyte progenitor, astrocyte and mesenchymal like states, and the relative frequency of each state varies between tumors [143]
Human Mononuclear tumor cells from one marrow cells 10× Genomics Identified mantle cell lymphoma subpopulations comprising malignant B cell subtypes, T cell subtypes, dendritic cell subtypes and natural killer cell subtypes, with different cell markers, including genes associated with immune escape and drug resistance [144]
Mononuclear cells from marrow 10× Genomics Reveal abnormalities of alternative polyadenylation dynamics in acute myeloid leukaemia and lower alternative polyadenylation diversity between eight cell types with regulation in leukemia development and erythropoiesis [145]
Epithelial cells from gastric mucosa 10× Genomics Construct a network for gastric epithelial cells across different lesions, with two conserved gastric mucous-secreting cells and enteroendocrine cells, and two subsets reflecting distinct cell states within goblet cells [146]
Tumor cells from oocyte/lymphocyte Smart-Seq Identify distinct gene expression patterns, including candidate biomarkers for melanoma circulating tumor cells [77]
Human/ mice CD8+ lymphocytes SMART-Seq2 Examinate of the dynamics of the development of the effector CD8+ T cell response in the TME in the context of immunotherapy suggests a central role for PD-1−CD8+ TILs [147]
Cancer cell from Pancreas 10× Genomics Demonstrate the presence of tumor microenvironment-associated genes, IL10Rβ, IL34 and CSF1R, in epithelial tumor cells, suggesting IL10Rβ and CSF1R may be ligand-dependent and the impact of ligand and receptor inhibition mirrored each other [148]
Tumor cells from cerebellum 10× Genomics Highlight different molecular subgroups of childhood cerebellar tumors mirror the transcription of cells from distinct, temporally restricted cerebellar lineages [149]
CD45+ cells from intratumor 10× Genomics Interferon signaling in cancer cells and immune cells oppose each other to establish a regulatory relationship that limits both adaptive and innate immune killing [150]
CD45+ cells from tumor 10× Genomics Uncover the topography and molecular profiles, with more abundant arginase 1+ macrophages in tumors and two morphologically distinct subsets of tumor-associated macrophages [151]
Lung progenitor cells 10× Genomics Demonstrate NOTCH signaling inhibition induces lung progenitor cells to form pulmonary neuroendocrine cells, and that subsequent interference with the P53 suppressor gene allows early-stage small cell lung cancers [152]
Tumor-associated mononuclear phagocytes 10× Genomics Reveal a unique subset with Fcmr-dependent heterogeneity, with Fcmr activity negatively regulating the activation and migratory capacity of myeloid cells, and T cell activation by bone marrow-derived dendritic cells [153]
Bone marrow cells 10× Genomics Define a previously unrecognized B1-box oligomerization in promyelocyticleukemia, but also highlight oligomerization as an important factor in carcinogenesis. [154]
Kelly cell 10× Genomics Identify a previously unrecognized role of brother of the regulator of imprinted sites, to promote regulatory chromatin interactions that support specific cancer phenotypes [155]
Tumor infiltration lymphocyte 10× Genomics Demonstrate high-throughput in vivo genetic screens for immunotherapy target discovery and establish DHX37 as a functional regulator of CD8 T cells [156]
Mice Stromal and tumor cells 10× Genomics Reveal discriminating chromatin features, either permissive or repressive for transcription, and characterize tumor heterogeneity at the level of chromatin features [157]
Medulloblastoma 10× Chromium Investigate heterogeneity in four consensus molecular subgroups (WNT, SHH, Group 3 and 4), while Group 3 tumours exhibit subgroup-specific cellular trajectories with malignant undifferentiated and differentiated neuronal-like populations, whereas Group 4 tumours recapitulate more differentiated populations of known lineage [158]
Cancer cell line models 10× Genomics Reveal significant heterogeneity that interferons stimulated genes-high population (cluster 7) enriched for cells expressing type Ⅰ and Ⅲ IFN transcripts, supporting that chronic interferons may be derived from cancer cells [159]
Leukaemic blasts from bone marrow 10× Genomics Reveal transcriptional plasticity driven stable epigenetic resistance between drug naïve and resistant acute myeloid leukaemia samples [160]
Tumor cells from endothelial 10× Genomics Find subtypes of endothelial cells in tumors, and fresh endothelial cell isolating from lung and mammary tumor models had differential abilities to degrade fibrin and launch new vessel sprouts [161]
Tumor cells from breast 10× Genomics Tumor resection had a major impact on reducing clonal diversity in secondary sites, indicating that most disseminated tumor cells lacked the capacity to ‘seed’, hence originated from ‘shedders’ that did not persist. [162]
Tumor cells from inguinal tissue 10× Genomics Identify responsive tumors were characterized by inflammatory gene expression consistent with upregulation of STAT1 and TLR3, and down-regulation of IL-10, and more infiltrating activated natural killer cells [163]
Others
Mice Pancreatic β cells Smart-seq2 Identify sex-dependent T2D altered genes and sexual dimorphism in T2D pathogenicity, suggesting sex-based differences in the pathological mechanisms of T2D [164]