Citation: | Baocai Xie, Dengfeng Gao, Biqiang Zhou, Shi Chen, Lianrong Wang. New discoveries in the field of metabolism by applying single-cell and spatial omics[J]. Journal of Pharmaceutical Analysis, 2023, 13(7): 711-725. doi: 10.1016/j.jpha.2023.06.002 |
F. Tang, C. Barbacioru, Y. Wang, et al., mRNA-Seq whole-transcriptome analysis of a single cell, Nat. Methods 6 (2009) 377-382.
|
A. Regev, S.A. Teichmann, E.S. Lander, et al., The human cell atlas, eLife 6 (2017), e27041.
|
Method of the year 2019:Single-cell multimodal omics, Nat. Meth. 17 (2020), 1.
|
V. Marx, Method of the year:Spatially resolved transcriptomics, Nat. Meth. 18 (2021) 9-14.
|
D.E. Wagner, A.M. Klein, Lineage tracing meets single-cell omics:Opportunities and challenges, Nat. Rev. Genet. 21 (2020) 410-427.
|
S. Vickovic, B. Lotstedt, J. Klughammer, et al., SM-Omics is an automated platform for high-throughput spatial multi-omics, Nat. Commun. 13 (2022), 795.
|
A. Chen, S. Liao, M. Cheng, et al., Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays, Cell 185 (2022) 1777-1792.e21.
|
A. Jourdon, S. Scuderi, D. Capauto, et al., PsychENCODE and beyond:Transcriptomics and epigenomics of brain development and organoids, Neuropsychopharmacology 46 (2021) 70-85.
|
S.Z. Wu, G. Al-Eryani, D.L. Roden, et al., A single-cell and spatially resolved atlas of human breast cancers, Nat. Genet. 53 (2021) 1334-1347.
|
J. Yang, M. Vamvini, P. Nigro, et al., Single-cell dissection of the obesity-exercise axis in adipose-muscle tissues implies a critical role for mesenchymal stem cells, Cell Metab. 34 (2022) 1578-1593. e6.
|
M. Fasolino, G.W. Schwartz, A.R. Patil, et al., Single-cell multi-omics analysis of human pancreatic islets reveals novel cellular states in type 1 diabetes, Nat. Metab. 4 (2022) 284-299.
|
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.
|
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.
|
I. Gaspar, A. Ephrussi, Strength in numbers:Quantitative single-molecule RNA detection assays, Wiley Interdiscip. Rev. 4 (2015) 135-150.
|
M. Asp, J. Bergenstråhle, J. Lundeberg, Spatially resolved transcriptomes-next generation tools for tissue exploration, Bioessays 42 (2020), e1900221.
|
X. Qian, K.D. Harris, T. Hauling, et al., Probabilistic cell typing enables fine mapping of closely related cell types in situ, Nat. Meth. 17 (2020) 101-106.
|
D. Jovic, X. Liang, H. Zeng, et al., Single-cell RNA sequencing technologies and applications:A brief overview, Clin. Transl. Med. 12 (2022), e694.
|
J. Park, J. Kim, T. Lewy, et al., Spatial omics technologies at multimodal and single cell/subcellular level, Genome Biol. 23 (2022), 256.
|
M. Stoeckius, C. Hafemeister, W. Stephenson, et al., Simultaneous epitope and transcriptome measurement in single cells, Nat. Methods 14 (2017) 865-868.
|
K. Zhang, M. Gao, Z. Chong, et al., Single-cell isolation by a modular single-cell pipette for RNA-sequencing, Lab Chip 16 (2016) 4742-4748.
|
I.C. MacAulay, W. Haerty, P. Kumar, et al., G&T-seq:Parallel sequencing of single-cell genomes and transcriptomes, Nat. Meth. 12 (2015) 519-522.
|
S. Pott, Simultaneous measurement of chromatin accessibility, DNA methylation, and nucleosome phasing in single cells, eLife 6 (2017), 23203.
|
S.A. Vitak, K.A. Torkenczy, J.L. Rosenkrantz, et al., Sequencing thousands of single-cell genomes with combinatorial indexing, Nat. Methods 14 (2017) 302-308.
|
T. Nagano, Y. Lubling, T.J. Stevens, et al., Single-cell Hi-C reveals cell-to-cell variability in chromosome structure, Nature 502 (2013) 59-64.
|
M. Zheng, S.Z. Tian, D. Capurso, et al., Multiplex chromatin interactions with single-molecule precision, Nature 566 (2019) 558-562.
|
V. Ramani, X. Deng, R. Qiu, et al., Sci-Hi-C:A single-cell Hi-C method for mapping 3D genome organization in large number of single cells, Methods 170 (2020) 61-68.
|
J.D. Buenrostro, B. Wu, U.M. Litzenburger, et al., Single-cell chromatin accessibility reveals principles of regulatory variation, Nature 523 (2015) 486-490.
|
D.A. Cusanovich, R. Daza, A. Adey, et al., Multiplex single cell profiling of chromatin accessibility by combinatorial cellular indexing, Science 348 (2015) 910-914.
|
B.B. Lake, S. Chen, B.C. Sos, et al., Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain, Nat. Biotechnol. 36 (2018) 70-80.
|
J. Cao, D.A. Cusanovich, V. Ramani, et al., Joint profiling of chromatin accessibility and gene expression in thousands of single cells, Science 361 (2018) 1380-1385.
|
C.A. Lareau, F.M. Duarte, J.G. Chew, et al., Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility, Nat. Biotechnol. 37 (2019) 916-924.
|
Y. Hu, Z. Jiang, K. Chen, et al., scNanoATAC-seq:A long-read single-cell ATAC sequencing method to detect chromatin accessibility and genetic variants simultaneously within an individual cell, Cell Res. 33 (2023) 83-86.
|
C. Luo, C.L. Keown, L. Kurihara, et al., Single-cell methylomes identify neuronal subtypes and regulatory elements in mammalian cortex, Science 357 (2017) 600-604.
|
H. Guo, P. Zhu, X. Wu, et al., Single-cell methylome landscapes of mouse embryonic stem cells and early embryos analyzed using reduced representation bisulfite sequencing, Genome Res. 23 (2013) 2126-2135.
|
S.A. Smallwood, H.J. Lee, C. Angermueller, et al., Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity, Nat. Methods 11 (2014) 817-820.
|
R.M. Mulqueen, D. Pokholok, S.J. Norberg, et al., Highly scalable generation of DNA methylation profiles in single cells, Nat. Biotechnol. 36 (2018) 428-431.
|
C. Angermueller, S.J. Clark, H.J. Lee, et al., Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity, Nat. Methods 13 (2016) 229-232.
|
G. Haimovich, J.E. Gerst, Single-molecule fluorescence in situ hybridization (smFISH) for RNA detection in adherent animal cells, Bio-protocol 8 (2018), e3070.
|
A. Butler, P. Hoffman, P. Smibert, et al., Integrating single-cell transcriptomic data across different conditions, technologies, and species, Nat. Biotechnol. 36 (2018) 411-420.
|
J.G. Gall, M.L. Pardue, Formation and detection of RNA-DNA hybrid molecules in cytological preparations, Proc. Natl. Acad. Sci. USA 63 (1969) 378-383.
|
M.L. Reyzer, P. Chaurand, P.M. Angel, et al., Direct molecular analysis of whole-body animal tissue sections by MALDI imaging mass spectrometry, Methods Mol Biol. 656 (2010) 285-301.
|
P. Lasch, W. Haensch, D. Naumann, et al., Imaging of colorectal adenocarcinoma using FT-IR microspectroscopy and cluster analysis, Biochim. Biophys. Acta BBA Mol. Basis Dis. 1688 (2004) 176-186.
|
P.O. Krutzik, G.P. Nolan, Fluorescent cell barcoding in flow cytometry allows high-throughput drug screening and signaling profiling, Nat. Methods 3 (2006) 361-368.
|
A. Gordon, A. Colman-Lerner, T.E. Chin, et al., Single-cell quantification of molecules and rates using open-source microscope-based cytometry, Nat. Meth. 4 (2007) 175-181.
|
D. Wang, S. Bodovitz, Single cell analysis:The new frontier in 'omics', Trends Biotechnol. 28 (2010) 281-290.
|
F. Porichis, M.G. Hart, M. Griesbeck, et al., High-throughput detection of miRNAs and gene-specific mRNA at the single-cell level by flow cytometry, Nat. Commun. 5 (2014), 5641.
|
D. Duan, K. Zheng, Y. Shen, et al., Label-free high-throughput microRNA expression profiling from total RNA, Nucleic Acids Res 39 (2011), e154.
|
K. Achim, J.B. Pettit, L.R. Saraiva, et al., High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin, Nat. Biotechnol. 33 (2015) 503-509.
|
R. Satija, J.A. Farrell, D. Gennert, et al., Spatial reconstruction of single-cell gene expression data, Nat. Biotechnol. 33 (2015) 495-502.
|
W.C. Boon, K. Petkovic-Duran, Y. Zhu, et al., Increasing cDNA yields from single-cell quantities of mRNA in standard laboratory reverse transcriptase reactions using acoustic microstreaming, J Vis Exp. (2011), e3144.
|
F. Wang, J. Flanagan, N. Su, et al., RNAscope:A novel in situ RNA analysis platform for formalin-fixed, paraffin-embedded tissues, J. Mol. Diagn. 14 (2012) 22-29.
|
R. Ke, M. Mignardi, A. Pacureanu, et al., In situ sequencing for RNA analysis in preserved tissue and cells, Nat. Methods 10 (2013) 857-860.
|
D. Lovatt, B.K. Ruble, J. Lee, et al., Transcriptome in vivo analysis (TIVA) of spatially defined single cells in live tissue, Nat. Meth. 11 (2014) 190-196.
|
J.H. Lee, E.R. Daugharthy, J. Scheiman, et al., Highly multiplexed subcellular RNA sequencing in situ, Science 343 (2014) 1360-1363.
|
J.P. Junker, E.S. Noel, V. Guryev, et al., Genome-wide RNA tomography in the zebrafish embryo, Cell 159 (2014) 662-675.
|
K.H. Chen, A.N. Boettiger, J.R. Moffitt, et al., RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells, Science 348 (2015), aaa6090.
|
P.L. Ståhl, F. Salmén, S. Vickovic, et al., Visualization and analysis of gene expression in tissue sections by spatial transcriptomics, Science 353 (2016) 78-82.
|
S. Shah, E. Lubeck, M. Schwarzkopf, et al., Single-molecule RNA detection at depth by hybridization chain reaction and tissue hydrogel embedding and clearing, Development 143 (2016) 2862-2867.
|
C. Medaglia, A. Giladi, L. Stoler-Barak, et al., Spatial reconstruction of immune niches by combining photoactivatable reporters and scRNA-seq, Science 358 (2017) 1622-1626.
|
S. Codeluppi, L.E. Borm, A. Zeisel, et al., Spatial organization of the somatosensory cortex revealed by osmFISH, Nat. Methods 15 (2018) 932-935.
|
X. Chen, Y.C. Sun, G.M. Church, et al., Efficient in situ barcode sequencing using padlock probe-based BaristaSeq, Nucleic Acids Res. 46 (2018), e22.
|
J. Chen, S. Suo, P.P. Tam, et al., Spatial transcriptomic analysis of cryosectioned tissue samples with Geo-seq, Nat. Protoc. 12 (2017) 566-580.
|
X. Wang, W.E. Allen, M.A. Wright, et al., Three-dimensional intact-tissue sequencing of single-cell transcriptional states, Science 361 (2018), eaat5691.
|
F.M. Fazal, S. Han, K.R. Parker, et al., Atlas of subcellular RNA localization revealed by APEX-seq, Cell 178 (2019) 473-490.e26.
|
J.R. Moffitt, D. Bambah-Mukku, S.W. Eichhorn, et al., Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region, Science 362 (2018), eaau5324.
|
S. Vickovic, G. Eraslan, F. Salmén, et al., High-definition spatial transcriptomics for in situ tissue profiling, Nat. Methods 16 (2019) 987-990.
|
C.L. Eng, M. Lawson, Q. Zhu, et al., Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH, Nature 568 (2019) 235-239.
|
K.H. Hu, J.P. Eichorst, C.S. McGinnis, et al., ZipSeq:Barcoding for real-time mapping of single cell transcriptomes, Nat. Methods 17 (2020) 833-843.
|
Y. Liu, M. Yang, Y. Deng, et al., High-spatial-resolution multi-omics sequencing via deterministic barcoding in tissue, Cell 183 (2020) 1665-1681.e18.
|
J.J.L. Goh, N. Chou, W.Y. Seow, et al., Highly specific multiplexed RNA imaging in tissues with split-FISH, Nat. Methods 17 (2020) 689-693.
|
Y. Lee, D. Bogdanoff, Y. Wang, et al., XYZeq:Spatially resolved single-cell RNA sequencing reveals expression heterogeneity in the tumor microenvironment, Sci. Adv. 7 (2021), eabg4755.
|
S.R. Srivatsan, M.C. Regier, E. Barkan, et al., Embryo-scale, single-cell spatial transcriptomics, Science 373 (2021) 111-117.
|
A.S. Genshaft, C.G.K. Ziegler, C.N. Tzouanas, et al., Live cell tagging tracking and isolation for spatial transcriptomics using photoactivatable cell dyes, Nat. Commun., 12 (2021), 4995
|
J.L. Marshall, T. Noel, Q.S. Wang, et al., High-resolution Slide-seqV2 spatial transcriptomics enables discovery of disease-specific cell neighborhoods and pathways, iScience 25 (2022), 104097.
|
C.S. Cho, J. Xi, Y. Si, et al., Microscopic examination of spatial transcriptome using Seq-Scope, Cell 184 (2021) 3559-3572.e22.
|
R. Moncada, D. Barkley, F. Wagner, et al., Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas, Nat. Biotechnol. 38 (2020) 333-342.
|
S.K. Longo, M.G. Guo, A.L. Ji, et al., Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics, Nat. Rev. Genet. 22 (2021) 627-644.
|
D. Dar, N. Dar, L. Cai, et al., Spatial transcriptomics of planktonic and sessile bacterial populations at single-cell resolution, Science 373 (2021), eabi4882.
|
Z. Zeng, Y. Li, Y. Li, et al., Statistical and machine learning methods for spatially resolved transcriptomics data analysis, Genome Biol. 23 (2022), 83.
|
R. Shen, L. Liu, Z. Wu, et al., Spatial-ID:A cell typing method for spatially resolved transcriptomics via transfer learning and spatial embedding, Nat. Commun. 13 (2022), 7640.
|
S. Eddy, L.H. Mariani, M. Kretzler, Integrated multi-omics approaches to improve classification of chronic kidney disease, Nat. Rev. Nephrol. 16 (2020) 657-668.
|
Y. Li, L. Ma, D. Wu, et al., Advances in bulk and single-cell multi-omics approaches for systems biology and precision medicine, Brief. Bioinform. 22 (2021), bbab024.
|
S.L. Friedman, M. Pinzani, Hepatic fibrosis 2022:Unmet needs and a blueprint for the future, Hepatology 75 (2022) 473-488.
|
L. Zhang, D. Chen, D. Song, et al., Clinical and translational values of spatial transcriptomics, Signal Transduct. Target. Ther. 7 (2022), 111.
|
A. Rosengren, Obesity and cardiovascular health:The size of the problem, Eur Heart J 42 (2021) 3404-3406.
|
M. Shao, L. Vishvanath, N.C. Busbuso, et al., De novo adipocyte differentiation from Pdgfrβ+ preadipocytes protects against pathologic visceral adipose expansion in obesity, Nat. Commun. 9 (2018), 890.
|
J.I. Kim, J.Y. Huh, J.H. Sohn, et al., Lipid-overloaded enlarged adipocytes provoke insulin resistance independent of inflammation, Mol. Cell. Biol. 35 (2015) 1686-1699.
|
K. Shinoda, I.H.N. Luijten, Y. Hasegawa, et al., Genetic and functional characterization of clonally derived adult human brown adipocytes, Nat. Med. 21 (2015) 389-394.
|
E.A. Rondini, J.G. Granneman, Single cell approaches to address adipose tissue stromal cell heterogeneity, Biochem. J. 477 (2020) 583-600.
|
T.K. Olsen, N. Baryawno, Introduction to single-cell RNA sequencing, Curr. Protoc. Mol. Biol. 122 (2018), e57.
|
P.C. Schwalie, H. Dong, M. Zachara, et al., A stromal cell population that inhibits adipogenesis in mammalian fat depots, Nature 559 (2018) 103-108.
|
C. Hepler, B. Shan, Q. Zhang, et al., Identification of functionally distinct fibro-inflammatory and adipogenic stromal subpopulations in visceral adipose tissue of adult mice, eLife 7 (2018), e39636.
|
D. Merrick, A. Sakers, Z. Irgebay, et al., Identification of a mesenchymal progenitor cell hierarchy in adipose tissue, Science 364 (2019), eaav2501.
|
A.D. Hildreth, F. Ma, Y.Y. Wong, et al., Single-cell sequencing of human white adipose tissue identifies new cell states in health and obesity, Nat. Immunol. 22 (2021) 639-653.
|
J. Vijay, M.F. Gauthier, R.L. Biswell, et al., Single-cell analysis of human adipose tissue identifies depot and disease specific cell types, Nat. Metab. 2 (2020) 97-109.
|
J.R. Acosta, S. Joost, K. Karlsson, et al., Single cell transcriptomics suggest that human adipocyte progenitor cells constitute a homogeneous cell population, Stem Cell Res. Ther. 8 (2017), 250.
|
M.P. Emont, C. Jacobs, A.L. Essene, et al., A single-cell atlas of human and mouse white adipose tissue, Nature 603 (2022) 926-933.
|
W. Gu, W.N. Nowak, Y. Xie, et al., Single-cell RNA-sequencing and metabolomics analyses reveal the contribution of perivascular adipose tissue stem cells to vascular remodeling, Arterioscler. Thromb. Vasc. Biol. 39 (2019) 2049-2066.
|
R.B. Burl, V.D. Ramseyer, E.A. Rondini, et al., Deconstructing adipogenesis induced by β3-adrenergic receptor activation with single-cell expression profiling, Cell Metab. 28 (2018) 300-309.e4.
|
D.S. Cho, B. Lee, J.D. Doles, Refining the adipose progenitor cell landscape in healthy and obese visceral adipose tissue using single-cell gene expression profiling, Life Sci. Alliance 2 (2019), e201900561.
|
W. Sun, H. Dong, M. Balaz, et al., snRNA-seq reveals a subpopulation of adipocytes that regulates thermogenesis, Nature 587 (2020) 98-102.
|
P. Rajbhandari, D. Arneson, S.K. Hart, et al., Single cell analysis reveals immune cell-adipocyte crosstalk regulating the transcription of thermogenic adipocytes, eLife 8 (2019), e49501.
|
A.K. Sárvári, E.L. Van Hauwaert, L.K. Markussen, et al., Plasticity of epididymal adipose tissue in response to diet-induced obesity at single-nucleus resolution, Cell Metab. 33 (2021) 437-453.e5.
|
K.L. Whytock, Y. Sun, A. Divoux, et al., Single cell full-length transcriptome of human subcutaneous adipose tissue reveals unique and heterogeneous cell populations, iScience 25 (2022), 104772.
|
J. Bäckdahl, L. Franzén, L. Massier, et al., Spatial mapping reveals human adipocyte subpopulations with distinct sensitivities to insulin, Cell Metab. 33 (2021) 1869-1882.e6.
|
P. Alonso-Magdalena, I. Quesada, A. Nadal, Endocrine disruptors in the etiology of type 2 diabetes mellitus, Nat. Rev. Endocrinol. 7 (2011) 346-353.
|
P. Saeedi, I. Petersohn, P. Salpea, et al., Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045:Results from the International Diabetes Federation Diabetes Atlas, 9th edition, Diabetes Res. Clin. Pract. 157 (2019), 107843.
|
M. Roden, G.I. Shulman, The integrative biology of type 2 diabetes, Nature 576 (2019) 51-60.
|
Y. Guo, Z. Huang, D. Sang, et al., The role of nutrition in the prevention and intervention of type 2 diabetes, Front. Bioeng. Biotechnol. 8 (2020), 575442.
|
D.M. Riddy, P. Delerive, R.J. Summers, et al., G protein-coupled receptors targeting insulin resistance, obesity, and type 2 diabetes mellitus, Pharmacol. Rev. 70 (2018) 39-67.
|
J. Almaça, A. Caicedo, L. Landsman, Beta cell dysfunction in diabetes:The islet microenvironment as an unusual suspect, Diabetologia 63 (2020) 2076-2085.
|
R. Arrojo e Drigo, Y. Ali, J. Diez, et al., New insights into the architecture of the islet of Langerhans:A focused cross-species assessment, Diabetologia 58 (2015) 2218-2228.
|
E.R. Unanue, Macrophages in endocrine glands, with emphasis on pancreatic islets. Myeloid Cells in Health and Disease:A Synthesis, Chapter 48, Wiley, Hoboken, 2017, pp. 825-831.
|
O. Cabrera, D.M. Berman, N.S. Kenyon, et al., The unique cytoarchitecture of human pancreatic islets has implications for islet cell function, Proc. Natl. Acad. Sci. U. S. A. 103 (2006) 2334-2339.
|
C.J. Lam, A.R. Cox, D.R. Jacobson, et al., Highly proliferative α-cell-related islet endocrine cells in human pancreata, Diabetes 67 (2018) 674-686.
|
M. Brissova, R. Haliyur, D. Saunders, et al., α cell function and gene expression are compromised in type 1 diabetes, Cell Rep. 22 (2018) 2667-2676.
|
C. Dai, M. Brissova, R.B. Reinert, et al., Pancreatic islet vasculature adapts to insulin resistance through dilation and not angiogenesis, Diabetes 62 (2013) 4144-4153.
|
S. Demir, P.P. Nawroth, S. Herzig, et al., Emerging targets in type 2 diabetes and diabetic complications, Adv. Sci. 8 (2021), 2100275.
|
D. Grün, A. van Oudenaarden, Design and analysis of single-cell sequencing experiments, Cell 163 (2015) 799-810.
|
R. Dorajoo, Y. Ali, V.S.Y. Tay, et al., Single-cell transcriptomics of east-asian pancreatic islets cells, Sci. Rep. 7 (2017), 5024.
|
Y.J. Wang, J. Schug, K.J. Won, et al., Single-cell transcriptomics of the human endocrine pancreas, Diabetes 65 (2016) 3028-3038.
|
M.J. Muraro, G. Dharmadhikari, D. Grun, et al., A single-cell transcriptome atlas of the human pancreas, Cell Syst. 3 (2016) 385-394.e3.
|
Å. Segerstolpe, A. Palasantza, P. Eliasson, et al., Single-cell transcriptome profiling of human pancreatic islets in health and type 2 diabetes, Cell Metab. 24 (2016) 593-607.
|
J. Li, J. Klughammer, M. Farlik, et al., Single-cell transcriptomes reveal characteristic features of human pancreatic islet cell types, EMBO Rep. 17 (2016) 178-187.
|
N. Lawlor, J. George, M. Bolisetty, et al., Single-cell transcriptomes identify human islet cell signatures and reveal cell-type-specific expression changes in type 2 diabetes, Genome Res. 27 (2017) 208-222.
|
A.T N. Lam, M.A. Aksit, B. Vecchio-Pagan, et al., Increased expression of anion transporter SLC26A9 delays diabetes onset in cystic fibrosis, J. Clin. Investig. 130 (2020) 272-286.
|
P.S. Linsley, F. Barahmand-Pour-Whitman, E. Balmas, et al., Autoreactive T cell receptors with shared germline-like alpha chains in type 1 diabetes, JCI Insight 6 (2021), e151349.
|
M. Saikia, M.M. Holter, L.R. Donahue, et al., GLP-1 receptor signaling increases PCSK1 and beta cell features in human alpha cells, JCI Insight 6 (2021), e141851.
|
C. Su, L. Gao, C.L. May, et al., 3D chromatin maps of the human pancreas reveal lineage-specific regulatory architecture of T2D risk, Cell Metab. 34 (2022) 1394-1409.e4.
|
D.E. Stanescu, R. Yu, K.J. Won, et al., Single cell transcriptomic profiling of mouse pancreatic progenitors, Physiol. Genom. 49 (2017) 105-114.
|
C. Zeng, F. Mulas, Y. Sui, et al., Pseudotemporal ordering of single cells reveals metabolic control of postnatal β cell proliferation, Cell Metab. 25 (2017) 1160-1175.e11.
|
N. Sharon, R. Chawla, J. Mueller, et al., A peninsular structure coordinates asynchronous differentiation with morphogenesis to generate pancreatic islets, Cell 176 (2019) 790-804.e13.
|
A.M. Hendley, A.A. Rao, L. Leonhardt, et al., Single-cell transcriptome analysis defines heterogeneity of the murine pancreatic ductal tree, eLife 10 (2021), 67776.
|
Y. Xin, J. Kim, M. Ni, et al., Use of the Fluidigm C1 platform for RNA sequencing of single mouse pancreatic islet cells, Proc. Natl. Acad. Sci. U. S. A. 113 (2016) 3293-3298.
|
G. Basile, S. Kahraman, E. Dirice, et al., Using single-nucleus RNA-sequencing to interrogate transcriptomic profiles of archived human pancreatic islets, Genome Med. 13 (2021), 128.
|
N.A.J. Krentz, M.Y.Y. Lee, E.E. Xu, et al., Single-cell transcriptome profiling of mouse and hESC-derived pancreatic progenitors, Stem Cell Rep. 11 (2018) 1551-1564.
|
D. Wang, J. Wang, L. Bai, et al., Long-term expansion of pancreatic islet organoids from resident procr+ progenitors, Cell 180 (2020) 1198-1211.e19.
|
S. Sachs, A. Bastidas-Ponce, S. Tritschler, et al., Targeted pharmacological therapy restores β-cell function for diabetes remission, Nat. Metab. 2 (2020) 192-209.
|
T. Fukaishi, Y. Nakagawa, A. Fukunaka, et al., Characterisation of Ppy-lineage cells clarifies the functional heterogeneity of pancreatic beta cells in mice, Diabetologia 64 (2021) 2803-2816.
|
P.N. Zakharov, H. Hu, X. Wan, et al., Single-cell RNA sequencing of murine islets shows high cellular complexity at all stages of autoimmune diabetes, J. Exp. Med. 217 (2020), e20192362.
|
P. Chen, F. Yao, Y. Lu, et al., Single-cell landscape of mouse islet allograft and syngeneic graft, Front. Immunol. 13 (2022), 853349.
|
E. Duvall, C.M. Benitez, K. Tellez, et al., Single-cell transcriptome and accessible chromatin dynamics during endocrine pancreas development, Proc Natl Acad Sci U S A. 119 (2022), e2201267119.
|
C. Sona, Y.T. Yeh, A. Patsalos, et al., Evidence of islet CADM1-mediated immune cell interactions during human type 1 diabetes, JCI Insight. 7 (2022), e153136.
|
Z.M. Younossi, A.B. Koenig, D. Abdelatif, et al., Global epidemiology of nonalcoholic fatty liver disease-Meta-analytic assessment of prevalence, incidence, and outcomes, Hepatology 64 (2016) 73-84.
|
Z. Younossi, Q.M. Anstee, M. Marietti, et al., Global burden of NAFLD and NASH:Trends, predictions, risk factors and prevention, Nat. Rev. Gastroenterol. Hepatol. 15 (2018) 11-20.
|
J.V. Lazarus, H.E. Mark, Q.M. Anstee, et al., Advancing the global public health agenda for NAFLD:A consensus statement, Nat. Rev. Gastroenterol. Hepatol. 19 (2022) 60-78.
|
J. Wang, W. He, P.J. Tsai, et al., Mutual interaction between endoplasmic reticulum and mitochondria in nonalcoholic fatty liver disease, Lipids Health Dis. 19 (2020), 72.
|
T.D. Challa, S. Wueest, F.C. Lucchini, et al., Liver ASK1 protects from non-alcoholic fatty liver disease and fibrosis, EMBO Mol Med. 11 (2019), e10124.
|
T.G. Cotter, M. Rinella, Nonalcoholic fatty liver disease 2020:The state of the disease, Gastroenterology 158 (2020) 1851-1864.
|
K.C. Sung, W.S. Jeong, S.H. Wild, et al., Combined influence of insulin resistance, overweight/obesity, and fatty liver as risk factors for type 2 diabetes, Diabetes Care 35 (2012) 717-722.
|
J. Cai, X. Zhang, Y. Ji, et al., Nonalcoholic fatty liver disease pandemic fuels the upsurge in cardiovascular diseases, Circ. Res. 126 (2020) 679-704.
|
E. Barreby, P. Chen, M. Aouadi, Macrophage functional diversity in NAFLD-More than inflammation, Nat. Rev. Endocrinol. 18 (2022) 461-472.
|
M.V. Machado, A.M. Diehl, Pathogenesis of nonalcoholic steatohepatitis, Gastroenterology 150 (2016) 1769-1777.
|
R. Tang, R. Li, H. Li, et al., Design of hepatic targeted drug delivery systems for natural products:Insights into nomenclature revision of nonalcoholic fatty liver disease, ACS Nano 15 (2021) 17016-17046.
|
O. Govaere, S. Cockell, D. Tiniakos, et al., Transcriptomic profiling across the nonalcoholic fatty liver disease spectrum reveals gene signatures for steatohepatitis and fibrosis, Sci. Transl. Med. 12 (2020), eaba4448.
|
B. Gapp, M. Jourdain, P. Bringer, et al., Farnesoid X receptor agonism, acetyl-coenzyme A carboxylase inhibition, and back translation of clinically observed endpoints of de novo lipogenesis in a murine NASH model, Hepatol. Commun. 4 (2020) 109-125.
|
Z. 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), 19396.
|
S.A. MacParland, J.C. Liu, X. Ma, et al., Single cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations, Nat. Commun. 9 (2018), 4383.
|
T.S. Andrews, J. Atif, J.C. Liu, et al., Single-cell, single-nucleus, and spatial RNA sequencing of the human liver identifies cholangiocyte and mesenchymal heterogeneity, Hepatol. Commun. 6 (2022) 821-840.
|
K. Diamanti, J.S. Inda Díaz, A. Raine, et al., Single nucleus transcriptomics data integration recapitulates the major cell types in human liver, Hepatol. Res. 51 (2021) 233-238.
|
R. Zhang, B. Zhong, J. He, et al., Single-cell transcriptomes identifies characteristic features of mouse macrophages in liver Mallory-Denk bodies formation, Exp. Mol. Pathol. 127 (2022), 104811.
|
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), 103233.
|
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.e5.
|
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.
|
J. Qing, Y. Ren, Y. Zhang, et al., Dopamine receptor D2 antagonism normalizes profibrotic macrophage-endothelial crosstalk in non-alcoholic steatohepatitis, J. Hepatol. 76 (2022) 394-406.
|
J.S. Seidman, T.D. Troutman, M. Sakai, et al., Niche-specific reprogramming of epigenetic landscapes drives myeloid cell diversity in nonalcoholic steatohepatitis, Immunity 52 (2020) 1057-1074.e7.
|
T. Gwag, R.G. Reddy Mooli, D. Li, et al., Macrophage-derived thrombospondin 1 promotes obesity-associated non-alcoholic fatty liver disease, JHEP Rep. 3 (2021), 100193.
|
S.R. Park, C.S. Cho, J. Xi, et al., Holistic characterization of single-hepatocyte transcriptome responses to high-fat diet, Am. J. Physiol. Endocrinol. Metab. 320 (2021) E244-E258.
|
H. Zhang, Y. Ma, X. Cheng, et al., Targeting epigenetically maladapted vascular niche alleviates liver fibrosis in nonalcoholic steatohepatitis, Sci. Transl. Med. 13 (2021), eabd1206.
|
R. Arena, C.J. Lavie, The global path forward-Healthy Living for Pandemic Event Protection (HL-PIVOT), Prog. Cardiovasc. Dis. 64 (2021) 96-101.
|
P. Libby, The changing landscape of atherosclerosis, Nature 592 (2021) 524-533.[LinkOut]
|
K.M. Pencina, G. Thanassoulis, J.T. Wilkins, et al., Trajectories of non-HDL cholesterol across midlife, J. Am. Coll. Cardiol. 74 (2019) 70-79.
|
J.S. Rana, S.M. Boekholdt, J.J.P. Kastelein, et al., The role of non-HDL cholesterol in risk stratification for coronary artery disease, Curr. Atheroscler. Rep. 14 (2012) 130-134.
|
G. Pilia, W.M. Chen, A. Scuteri, et al., Heritability of cardiovascular and personality traits in 6,148 Sardinians, PLoS Genet. 2 (2006), e132.
|
M.S. Brown, J.L. Goldstein, A receptor-mediated pathway for cholesterol homeostasis, Science 232 (1986) 34-47.
|
J. Defesche, Low-density lipoprotein receptor-its structure, function, and mutations, Semin. Vasc. Med. 4 (2004) 5-11.
|
J. Wang, L. Li, A. Hu, et al., Inhibition of ASGR1 decreases lipid levels by promoting cholesterol excretion, Nature 608 (2022) 413-420.
|
B. Xie, X. Shi, Y. Li, et al., Deficiency of ASGR1 in pigs recapitulates reduced risk factor for cardiovascular disease in humans, PLoS Genet. 17 (2021), e1009891.
|
M. Abifadel, M. Varret, J.P. Rabes, et al., Mutations in PCSK9 cause autosomal dominant hypercholesterolemia, Nat. Genet. 34 (2003) 154-156.
|
T.A. Lagace, D.E. Curtis, R. Garuti, et al., Secreted PCSK9 decreases the number of LDL receptors in hepatocytes and in livers of parabiotic mice, J. Clin. Invest. 116 (2006) 2995-3005.
|
Y.Y. Zhang, Z.Y. Fu, J. Wei, et al., A LIMA1 variant promotes low plasma LDL cholesterol and decreases intestinal cholesterol absorption, Science 360 (2018) 1087-1092.
|
H.R. Davis, E.P. Veltri, Zetia:Inhibition of niemann-pick C1 like 1 (NPC1L1) to reduce intestinal cholesterol absorption and treat hyperlipidemia, J. Atheroscler. Thromb. 14 (2007) 99-108.
|
H.R. Davis, L. Zhu, L.M. Hoos, et al., Niemann-pick C1 like 1 (NPC1L1) is the intestinal phytosterol and cholesterol transporter and a key modulator of whole-body cholesterol homeostasis, J. Biol. Chem. 279 (2004) 33586-33592.
|
N.R. Tucker, M. Chaffin, S.J. Fleming, et al., Transcriptional and cellular diversity of the human heart, Circulation 142 (2020) 466-482.
|
M. Litviňuková, C. Talavera-López, H. Maatz, et al., Cells of the adult human heart, Nature 588 (2020) 466-472.
|
L. Wang, P. Yu, B. Zhou, et al., Single-cell reconstruction of the adult human heart during heart failure and recovery reveals the cellular landscape underlying cardiac function, Nat. Cell Biol. 22 (2020) 108-119.
|
Y. Cui, Y. Zheng, X. Liu, et al., Single-cell transcriptome analysis maps the developmental track of the human heart, Cell Rep. 26 (2019) 1934-1950.e5.
|
M. Asp, S. Giacomello, L. Larsson, et al., A spatiotemporal organ-wide gene expression and cell atlas of the developing human heart, Cell 179 (2019) 1647-1660.e19.
|
F. Lescroart, X. Wang, X. Lin, et al., Defining the earliest step of cardiovascular lineage segregation by single-cell RNA-seq, Science 359 (2018) 1177-1181.
|
M. Wehrens, A.E. de Leeuw, M. Wright-Clark, et al., Single-cell transcriptomics provides insights into hypertrophic cardiomyopathy, Cell Rep. 39 (2022), 110809.
|
D.M. Fernandez, A.H. Rahman, N.F. Fernandez, et al., Single-cell immune landscape of human atherosclerotic plaques, Nat. Med. 25 (2019) 1576-1588.
|
Z. Zhang, J. Huang, Y. Wang, et al., Transcriptome analysis revealed a two-step transformation of vascular smooth muscle cells to macrophage-like cells, Atherosclerosis 346 (2022) 26-35.
|
H. Winkels, E. Ehinger, M. Vassallo, et al., Atlas of the immune cell repertoire in mouse atherosclerosis defined by single-cell RNA-sequencing and mass cytometry, Circ Res. 122 (2018) 1675-1688.
|
C. Cochain, E. Vafadarnejad, P. Arampatzi, et al., Single-cell RNA-seq reveals the transcriptional landscape and heterogeneity of aortic macrophages in murine atherosclerosis, Circ. Res. 122 (2018) 1661-1674.
|
L. He, M. Vanlandewijck, M.A. Mae, et al., Single-cell RNA sequencing of mouse brain and lung vascular and vessel-associated cell types, Sci. Data 5 (2018), 180160.
|
N. Farbehi, R. Patrick, A. Dorison, et al., Single-cell expression profiling reveals dynamic flux of cardiac stromal, vascular and immune cells in health and injury, eLife 8 (2019), e43882.
|
J. Kalucka, L.P.M.H. de Rooij, J. Goveia, et al., Single-cell transcriptome atlas of murine endothelial cells, Cell 180 (2020) 764-779.e20.
|
J. Rodor, S.H. Chen, J.P. Scanlon, et al., Single-cell RNA sequencing profiling of mouse endothelial cells in response to pulmonary arterial hypertension, Cardiovasc Res. 118 (2022) 2519-2534.
|
A. Zernecke, H. Winkels, C. Cochain, et al., Meta-analysis of leukocyte diversity in atherosclerotic mouse aortas, Circ. Res. 127 (2020) 402-426.
|
J. Lin, H. Nishi, J. Poles, et al., Single-cell analysis of fate-mapped macrophages reveals heterogeneity, including stem-like properties, during atherosclerosis progression and regression, JCI Insight 4 (2019), e124574.
|
J.E. Cole, I. Park, D.J. Ahern, et al., Immune cell census in murine atherosclerosis:Cytometry by time of flight illuminates vascular myeloid cell diversity, Cardiovasc Res. 114 (2018) 1360-1371.
|
G. Zhao, H. Lu, Y. Liu, et al., Single-cell transcriptomics reveals endothelial plasticity during diabetic atherogenesis, Front. Cell Dev. Biol. 9 (2021), 689469.
|
L. Yu, J. Zhang, A. Gao, et al., An intersegmental single-cell profile reveals aortic heterogeneity and identifies a novel Malat1+ vascular smooth muscle subtype involved in abdominal aortic aneurysm formation, Signal Transduct. Target. Ther. 7 (2022), 125.
|
W. Zhang, S. Zhang, P. Yan, et al., A single-cell transcriptomic landscape of primate arterial aging, Nat. Commun. 11 (2020), 2202.
|
L. Han, X. Wei, C. Liu, et al., Cell transcriptomic atlas of the non-human primate Macaca fascicularis, Nature 604 (2022) 723-731.
|
J. Qu, F. Yang, T. Zhu, et al., A reference single-cell regulomic and transcriptomic map of cynomolgus monkeys, Nat. Commun. 13 (2022), 4069.
|
Y. Li, Y. Cao, F. Liu, et al., Visualization and analysis of gene expression in stanford type A aortic dissection tissue section by spatial transcriptomics, Front. Genet. 12 (2021), 698124.
|
C. Kuppe, R.O. Ramirez Flores, Z. Li, et al., Spatial multi-omic map of human myocardial infarction, Nature 608 (2022) 766-777.
|
S. McArdle, K. Buscher, Y. Ghosheh, et al., Migratory and dancing macrophage subsets in atherosclerotic lesions, Circ. Res. 125 (2019) 1038-1051.
|
E.I. Crosse, S. Gordon-Keylock, S. Rybtsov, et al., Multi-layered spatial transcriptomics identify secretory factors promoting human hematopoietic stem cell development, Cell Stem Cell 27 (2020) 822-839.e8.
|
M. Mohenska, N.M. Tan, A. Tokolyi, et al., 3D-cardiomics:A spatial transcriptional atlas of the mammalian heart, J. Mol. Cell. Cardiol. 163 (2022) 20-32.
|
C. Tay, P. Kanellakis, H. Hosseini, et al., B cell and CD4 T cell interactions promote development of atherosclerosis, Front. Immunol. 10 (2019), 3046.
|
G.K. Hansson, P. Libby, The immune response in atherosclerosis:A double-edged sword, Nat. Rev. Immunol. 6 (2006) 508-519.
|
J.L. Stöger, M.J. Gijbels, S. van der Velden, et al., Distribution of macrophage polarization markers in human atherosclerosis, Atherosclerosis 225 (2012) 461-468.
|
H. Winkels, E. Ehinger, Y. Ghosheh, et al., Atherosclerosis in the single-cell era, Curr. Opin. Lipidol. 29 (2018) 389-396.
|
Z. Song, P. Gao, X. Zhong, et al., Identification of five hub genes based on single-cell RNA sequencing data and network pharmacology in patients with acute myocardial infarction, Front. Public Heath 10 (2022), 894129.
|