Single-cell transcriptomic and spatial landscapes of the developing human pancreas DOI Creative Commons
Oladapo E. Olaniru, Ulrich D. Kadolsky, Shichina Kannambath

et al.

Cell Metabolism, Journal Year: 2022, Volume and Issue: 35(1), P. 184 - 199.e5

Published: Dec. 12, 2022

Current differentiation protocols have not been successful in reproducibly generating fully functional human beta cells vitro, partly due to incomplete understanding of pancreas development. Here, we present detailed transcriptomic analysis the various cell types developing pancreas, including their spatial gene patterns. We integrated single-cell RNA sequencing with transcriptomics at multiple developmental time points and revealed distinct temporal-spatial cascades. Cell trajectory inference identified endocrine progenitor populations branch-specific genes as progenitors differentiate toward alpha or cells. Spatial trajectories indicated that Schwann are spatially co-located progenitors, cell-cell connectivity predicted they may interact via L1CAM-EPHB2 signaling. Our approach enabled us identify heterogeneity lineage dynamics within mesenchyme, showing it contributed exocrine acinar state. Finally, generated an interactive web resource for investigating development research community.

Language: Английский

Single-cell atlases: shared and tissue-specific cell types across human organs DOI
Rasa Elmentaite, Cecilia Domínguez Conde, Lu Yang

et al.

Nature Reviews Genetics, Journal Year: 2022, Volume and Issue: 23(7), P. 395 - 410

Published: Feb. 25, 2022

Language: Английский

Citations

131
Statistical and machine learning methods for spatially resolved transcriptomics data analysis DOI Creative Commons
Zexian Zeng, Yawei Li, Yiming Li

et al.

Genome biology, Journal Year: 2022, Volume and Issue: 23(1)

Published: March 25, 2022

Abstract The recent advancement in spatial transcriptomics technology has enabled multiplexed profiling of cellular transcriptomes and locations. As the capacity efficiency experimental technologies continue to improve, there is an emerging need for development analytical approaches. Furthermore, with continuous evolution sequencing protocols, underlying assumptions current methods be re-evaluated adjusted harness increasing data complexity. To motivate aid future model development, we herein review statistical machine learning transcriptomics, summarize useful resources, highlight challenges opportunities ahead.

Language: Английский

Citations

129
Modeling intercellular communication in tissues using spatial graphs of cells DOI Creative Commons
David S. Fischer, Anna C. Schaar, Fabian J. Theis

et al.

Nature Biotechnology, Journal Year: 2022, Volume and Issue: 41(3), P. 332 - 336

Published: Oct. 27, 2022

Abstract Models of intercellular communication in tissues are based on molecular profiles dissociated cells, limited to receptor–ligand signaling and ignore spatial proximity situ. We present node-centric expression modeling, a method graph neural networks that estimates the effects niche composition gene an unbiased manner from profiling data. recover signatures processes known underlie cell communication.

Language: Английский

Citations

127
Clinical and translational values of spatial transcriptomics DOI Creative Commons
Linlin Zhang, Dongsheng Chen, Dongli Song

et al.

Signal Transduction and Targeted Therapy, Journal Year: 2022, Volume and Issue: 7(1)

Published: April 1, 2022

Abstract The combination of spatial transcriptomics (ST) and single cell RNA sequencing (scRNA-seq) acts as a pivotal component to bridge the pathological phenomes human tissues with molecular alterations, defining in situ intercellular communications knowledge on spatiotemporal medicine. present article overviews development ST aims evaluate clinical translational values for understanding pathogenesis uncovering disease-specific biomarkers. We compare advantages disadvantages sequencing- imaging-based technologies highlight opportunities challenges ST. also describe bioinformatics tools necessary dissecting patterns gene expression cellular interactions potential applications diseases practice one important issues medicine, including neurology, embryo development, oncology, inflammation. Thus, clear objectives, designs, optimizations sampling procedure protocol, repeatability ST, well simplifications analysis interpretation are key translate from bench clinic.

Language: Английский

Citations

122
Progesterone Actions and Resistance in Gynecological Disorders DOI Creative Commons
James A. MacLean, Kanako Hayashi

Cells, Journal Year: 2022, Volume and Issue: 11(4), P. 647 - 647

Published: Feb. 13, 2022

Estrogen and progesterone their signaling mechanisms are tightly regulated to maintain a normal menstrual cycle support successful pregnancy. The imbalance of estrogen disrupts complex regulatory mechanisms, leading dominance resistance. Gynecological diseases heavily associated with dysregulated steroid hormones can induce chronic pelvic pain, dysmenorrhea, dyspareunia, heavy bleeding, infertility, which substantially impact the quality women's lives. Because repeatably occurs during reproductive ages dynamic changes remodeling reproductive-related tissues, these alterations accumulate recurrent conditions. This review focuses on faulty cellular responses in endometriosis, adenomyosis, leiomyoma (uterine fibroids), polycystic ovary syndrome (PCOS), endometrial hyperplasia. We also summarize association gene mutations hormone regulation disease progression as well current hormonal therapies clinical consequences

Language: Английский

Citations

109
Single-cell analysis of endometriosis reveals a coordinated transcriptional programme driving immunotolerance and angiogenesis across eutopic and ectopic tissues DOI
Yuliana Tan, William F. Flynn, Santhosh Sivajothi

et al.

Nature Cell Biology, Journal Year: 2022, Volume and Issue: 24(8), P. 1306 - 1318

Published: July 21, 2022

Language: Английский

Citations

104
Uterine bleeding: how understanding endometrial physiology underpins menstrual health DOI Open Access
Varsha Jain, Rohan Chodankar, Jacqueline A. Maybin

et al.

Nature Reviews Endocrinology, Journal Year: 2022, Volume and Issue: 18(5), P. 290 - 308

Published: Feb. 8, 2022

Language: Английский

Citations

99
Multi-omic analyses of changes in the tumor microenvironment of pancreatic adenocarcinoma following neoadjuvant treatment with anti-PD-1 therapy DOI Creative Commons
Keyu Li, Joseph A. Tandurella,

Jessica Gai

et al.

Cancer Cell, Journal Year: 2022, Volume and Issue: 40(11), P. 1374 - 1391.e7

Published: Oct. 27, 2022

Successful pancreatic ductal adenocarcinoma (PDAC) immunotherapy necessitates optimization and maintenance of activated effector T cells (Teff). We prospectively collected applied multi-omic analyses to paired pre- post-treatment PDAC specimens in a platform neoadjuvant study granulocyte-macrophage colony-stimulating factor-secreting allogeneic vaccine (GVAX) ± nivolumab (anti-programmed cell death protein 1 [PD-1]) uncover sensitivity resistance mechanisms. show that GVAX-induced tertiary lymphoid aggregates become immune-regulatory sites response GVAX + nivolumab. Higher densities tumor-associated neutrophils (TANs) following portend poorer overall survival (OS). Increased expressing CD137 associated with cytotoxic Teff signatures correlated increased OS. Bulk single-cell RNA sequencing found alters CD4+ chemotaxis signaling association CD11b+ neutrophil degranulation, CD8+ expression was required for optimal activation. These findings provide insights into PD-1-regulated immune pathways should inform more effective therapeutic combinations include TAN regulators activators.

Language: Английский

Citations

97
Evaluation of cell-cell interaction methods by integrating single-cell RNA sequencing data with spatial information DOI Creative Commons
Zhaoyang Liu, Dongqing Sun, Chenfei Wang

et al.

Genome biology, Journal Year: 2022, Volume and Issue: 23(1)

Published: Oct. 17, 2022

Abstract Background Cell-cell interactions are important for information exchange between different cells, which the fundamental basis of many biological processes. Recent advances in single-cell RNA sequencing (scRNA-seq) enable characterization cell-cell using computational methods. However, it is hard to evaluate these methods since no ground truth provided. Spatial transcriptomics (ST) data profiles relative position cells. We propose that spatial distance suggests interaction tendency cell types, thus could be used evaluating tools. Results benchmark 16 by integrating scRNA-seq with ST data. characterize into short-range and long-range distributions ligands receptors. Based on this classification, we define enrichment score apply an evaluation workflow tools 15 simulated 5 real datasets. also compare consistency results from single commonly identified interactions. Our suggest predicted highly dynamic, statistical-based show overall better performance than network-based ST-based Conclusions study presents a comprehensive scRNA-seq. CellChat, CellPhoneDB, NicheNet, ICELLNET other terms software scalability. recommend at least two ensure accuracy have packaged detailed documentation GitHub ( https://github.com/wanglabtongji/CCI ).

Language: Английский

Citations

94
CellChat for systematic analysis of cell-cell communication from single-cell and spatially resolved transcriptomics DOI Creative Commons
Suoqin Jin, Maksim V. Plikus,

Qing Nie

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Nov. 5, 2023

Abstract Recent advances in single-cell sequencing technologies offer an opportunity to explore cell-cell communication tissues systematically and with reduced bias. A key challenge is the integration between known molecular interactions measurements into a framework identify analyze complex networks. Previously, we developed computational tool, named CellChat that infers analyzes networks from RNA-sequencing (scRNA-seq) data within easily interpretable framework. quantifies signaling probability two cell groups using simplified mass action-based model, which incorporates core interaction ligands receptors multi-subunit structure along modulation by cofactors. v2 updated version includes direct incorporation of spatial locations cells, if available, infer spatially proximal communication, additional comparison functionalities, expanded database ligand-receptor pairs rich annotations, Interactive Explorer. Here provide step-by-step protocol for can be used both scRNA-seq resolved transcriptomic data, including inference analysis one dataset identification altered across different datasets. The steps applying transcriptomics are described detail. R implementation toolkit tutorials graphic outputs available at https://github.com/jinworks/CellChat . This typically takes around 20 minutes, no specialized prior bioinformatics training required complete task.

Language: Английский

Citations

94