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1. Single-Cell Transcriptomics Reveals Evolutionary Reconfiguration of Embryonic Cell Fate Specification in the Sea Urchin Heliocidaris erythrogramma

   https://doi.org/10.1093/gbe/evae258  

   Massri, Abdull J; Berrio, Alejandro; Afanassiev, Anton; Greenstreet, Laura; Pipho, Krista; Byrne, Maria; Schiebinger, Geoffrey; McClay, David R; Wray, Gregory A (January 2025, Genome Biology and Evolution)

   Fernandez-Valverde, Selene (Ed.)

   Abstract Altered regulatory interactions during development likely underlie a large fraction of phenotypic diversity within and between species, yet identifying specific evolutionary changes remains challenging. Analysis of single-cell developmental transcriptomes from multiple species provides a powerful framework for unbiased identification of evolutionary changes in developmental mechanisms. Here, we leverage a “natural experiment” in developmental evolution in sea urchins, where a major life history switch recently evolved in the lineage leading to Heliocidaris erythrogramma, precipitating extensive changes in early development. Comparative analyses of single-cell transcriptome analysis (scRNA-seq) developmental time courses from H. erythrogramma and Lytechinus variegatus (representing the derived and ancestral states, respectively) reveal numerous evolutionary changes in embryonic patterning. The earliest cell fate specification events and the primary signaling center are co-localized in the ancestral developmental gene regulatory network; remarkably, in H. erythrogramma, they are spatially and temporally separate. Fate specification and differentiation are delayed in most embryonic cell lineages, although in some cases, these processes are conserved or even accelerated. Comparative analysis of regulator-target gene co-expression is consistent with many specific interactions being preserved but delayed in H. erythrogramma, while some otherwise widely conserved interactions have likely been lost. Finally, specific patterning events are directly correlated with evolutionary changes in larval morphology, suggesting that they are directly tied to the life history shift. Together, these findings demonstrate that comparative scRNA-seq developmental time courses can reveal a diverse set of evolutionary changes in embryonic patterning and provide an efficient way to identify likely candidate regulatory interactions for subsequent experimental validation. 

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2. TraSig: inferring cell-cell interactions from pseudotime ordering of scRNA-Seq data

   https://doi.org/10.1186/s13059-022-02629-7  

   Li, Dongshunyi; Velazquez, Jeremy J.; Ding, Jun; Hislop, Joshua; Ebrahimkhani, Mo R.; Bar-Joseph, Ziv (December 2022, Genome Biology)

   Abstract A major advantage of single cell RNA-sequencing (scRNA-Seq) data is the ability to reconstruct continuous ordering and trajectories for cells. Here we present TraSig, a computational method for improving the inference of cell-cell interactions in scRNA-Seq studies that utilizes the dynamic information to identify significant ligand-receptor pairs with similar trajectories, which in turn are used to score interacting cell clusters. We applied TraSig to several scRNA-Seq datasets and obtained unique predictions that improve upon those identified by prior methods. Functional experiments validate the ability of TraSig to identify novel signaling interactions that impact vascular development in liver organoids. Software https://github.com/doraadong/TraSig . 

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3. Single-cell transcriptomics reveals a conserved metaplasia program in pancreatic injury

   https://doi.org/10.1101/2021.04.09.439243  

   Ma, Zhibo; Lytle, Nikki; Chen, Bob; Jyotsana, Nidhi; Novak, Sammy; Cho, Charles J; Caplan, Leah; Ben-Levy, Olivia; Neininger, Abigail C; Burnette, Dylan T; et al (April 2021, bioRxiv)

   Acinar to ductal metaplasia (ADM) occurs in the pancreas in response to tissue injury and is a potential precursor for adenocarcinoma. The goal of these studies was to define the populations arising from genetically wild type ADM, the associated transcriptionalchanges, and to identify markersof disease progression. Acinar cells were lineage-traced with enhanced yellow fluorescent protein (EYFP)to follow their fate upon injury. Transcripts of over 13,000 EYFP+ cells were determined using single-cellRNA sequencing (scRNA-seq). Single-celltrajectories were generated. Data were compared to gastric metaplasia and human pancreatitis. Results were confirmed by immunostaining and electron microscopy. Surgical specimens of chronic pancreatitis from 15 patients were evaluatedby immunostaining. scRNA-seq of ADM revealed emergence of a mucin/ductal population (Muc6+, Tff2+) resembling gastric pyloric, gland-base cells. Lineage trajectories suggest that this pyloric metaplasia is an intermediate cell identity between acinar cells and the generation of metaplastic tuft and enteroendocrine cells (EECs). 3-D electron microscopy demonstrates that all identified lineages populate ADM lesions. EECs exhibit substantial heterogeneity, including emergence of enterochromaffin (5-HT+) and delta (SST+) cells. Human pancreatitis shows a similar pyloric metaplasia phenotype anda conserved transcriptional program. Under conditions of chronic injury, acinar cells undergo pyloric metaplasia to mucinous progenitor-like populations, some of which can then seed disparate tuft cell and EEC lineages. EEC subtypes are diverse with the potential to direct disease progression. This program is conserved in human pancreatitis, providing insightinto early events in pancreasdiseases. 

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4. A data denoising approach to optimize functional clustering of single cell RNA-sequencing data

   https://doi.org/10.1109/BIBM49941.2020.9313483  

   Wan, Changlin; Jia, Dongya; Zhao, Yue; Chang, Wennan; Cao, Sha; Wang, Xiao; Zhang, Chi (December 2020, Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020)

   null (Ed.)

   Single cell RNA-sequencing (scRNA-seq) technology enables comprehensive transcriptomic profiling of thousands of cells with distinct phenotypic and physiological states in a complex tissue. Substantial efforts have been made to characterize single cells of distinct identities from scRNA-seq data, including various cell clustering techniques. While existing approaches can handle single cells in terms of different cell (sub)types at a high resolution, identification of the functional variability within the same cell type remains unsolved. In addition, there is a lack of robust method to handle the inter-subject variation that often brings severe confounding effects for the functional clustering of single cells. In this study, we developed a novel data denoising and cell clustering approach, namely CIBS, to provide biologically explainable functional classification for scRNA-seq data. CIBS is based on a systems biology model of transcriptional regulation that assumes a multi-modality distribution of the cells’ activation status, and it utilizes a Boolean matrix factorization approach on the discretized expression status to robustly derive functional modules. CIBS is empowered by a novel fast Boolean Matrix Factorization method, namely PFAST, to increase the computational feasibility on large scale scRNA-seq data. Application of CIBS on two scRNA-seq datasets collected from cancer tumor micro-environment successfully identified subgroups of cancer cells with distinct expression patterns of epithelial-mesenchymal transition and extracellular matrix marker genes, which was not revealed by the existing cell clustering analysis tools. The identified cell groups were significantly associated with the clinically confirmed lymph-node invasion and metastasis events across different patients. Index Terms—Cell clustering analysis, Data denoising, Boolean matrix factorization, Cancer microenvirionment, Metastasis. 

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5. Detection of differentially abundant cell subpopulations in scRNA-seq data

   https://doi.org/10.1073/pnas.2100293118  

   Zhao, Jun; Jaffe, Ariel; Li, Henry; Lindenbaum, Ofir; Sefik, Esen; Jackson, Ruaidhrí; Cheng, Xiuyuan; Flavell, Richard A.; Kluger, Yuval (May 2021, Proceedings of the National Academy of Sciences)

   null (Ed.)

   Comprehensive and accurate comparisons of transcriptomic distributions of cells from samples taken from two different biological states, such as healthy versus diseased individuals, are an emerging challenge in single-cell RNA sequencing (scRNA-seq) analysis. Current methods for detecting differentially abundant (DA) subpopulations between samples rely heavily on initial clustering of all cells in both samples. Often, this clustering step is inadequate since the DA subpopulations may not align with a clear cluster structure, and important differences between the two biological states can be missed. Here, we introduce DA-seq, a targeted approach for identifying DA subpopulations not restricted to clusters. DA-seq is a multiscale method that quantifies a local DA measure for each cell, which is computed from its k nearest neighboring cells across a range of k values. Based on this measure, DA-seq delineates contiguous significant DA subpopulations in the transcriptomic space. We apply DA-seq to several scRNA-seq datasets and highlight its improved ability to detect differences between distinct phenotypes in severe versus mildly ill COVID-19 patients, melanomas subjected to immune checkpoint therapy comparing responders to nonresponders, embryonic development at two time points, and young versus aging brain tissue. DA-seq enabled us to detect differences between these phenotypes. Importantly, we find that DA-seq not only recovers the DA cell types as discovered in the original studies but also reveals additional DA subpopulations that were not described before. Analysis of these subpopulations yields biological insights that would otherwise be undetected using conventional computational approaches. 

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