Biology subjects

Cahan, P.

Publications and source records attributed to Cahan, P..

28 records · Page 2Linked to original sources

bioRxiv2021

Quantitative comparison of in vitro and in vivo embryogenesis at a single cell resolution

Stem cell engineering is a powerful platform to address numerous challenges in regenerative medicine and developmental biology. Typically, engineered populations are derived by exposing pluripotent stem cells to a series of signaling cues meant to recapitulate developmental milestones, such as the induction of the primitive streak. Morphologic, genetic, transcriptomic, and functional differences between fully differentiated in vivo and engineered populations have long been recognized. However, the correspondence between engineered and native embryonic progenitors has been relatively less well characterized, largely due to their transient and highly heterogenous nature, and therefore many studies have relied on expression of a few canonical markers to ensure that their cells are on the correct path. To address this challenge, we first generated an in vivo gastrulation mouse single cell RNA sequencing (scRNA-seq) reference data set and used it to train a collection of computational tools for comparing cell types, states, regulators, and trajectories across studies. Then we used these tools to evaluate a commonly used protocol designed to promote mesoderm derivation, as well as four previously published biomimetic protocols. Finally, we diversified our toolkits to reach a broader scientific community by implementing our primary analysis tool in Python and as an interactive web application.

developmental biology↗

bioRxiv2021

Reconstruction of dynamic regulatory networks reveals signaling-induced topology changes associated with germ layer specification

Elucidating regulatory relationships between transcription factors (TFs) and target genes is fundamental to understanding how cells control their identity and behavior. Computational gene regulatory network (GRN) reconstruction methods aim to map this control by inferring relationships from transcriptomic data. Unfortunately, existing methods are imprecise, may be computationally burdensome, and do not uncover how networks transition from one topology to another. Here we present Epoch, a computational network reconstruction tool that leverages single cell transcriptomics to infer dynamic network structures. Epoch performs favorably when benchmarked using data derived from in vivo, in vitro, and in silico sources. To illustrate the usefulness of Epoch, we applied it to identify the dynamic networks underpinning directed differentiation of mouse embryonic stem cells (ESC) guided by multiple primitive streak induction treatments. Our analysis demonstrates that modulating signaling pathways drives topological network changes that shape cell fate potential. We also find that Peg3 is a central contributor to the rewiring of the pluripotency network to favor mesoderm specification. By integrating signaling pathways with GRN structures, we traced how Wnt activation and PI3K suppression govern mesoderm and endoderm specification, respectively. Finally, we compare the networks established in in vitro directed differentiation of ESCs to those in in vivo gastrulation and mesoderm specification. The methods presented here are available in the R package Epoch, and provide a foundation for future work in understanding the biological implications of dynamic regulatory structures.

systems biology↗

bioRxiv2020

Domino: reconstructing intercellular signaling dynamics with transcription factor activation in model biomaterial environments

Biomaterials serve as the basis of implants, tissue engineering scaffolds, and multiple other biomedical therapeutics. New technologies, such as single cell RNA sequencing (scRNAseq), are enabling characterization of the biomaterial response to an unprecedented level of detail, facilitating new discoveries in the complex cellular environment surrounding materials. We performed scRNAseq and integrated data sets from multiple experiments to create a single cell atlas of the biomaterials response that contains 42,156 cells from biological extracellular matrix (ECM)-derived and synthetic polyester (polycaprolactone, PCL) scaffold biomaterials implanted in murine muscle wounds. We identified 18 clusters of cells, including natural killer (NK) cells, multiple subsets of fibroblasts, and myeloid cells, many of which were previously unknown in the biomaterial response. To determine intra and intercellular signaling occurring between the numerous cell subsets, including immune-stromal interactions in the biomaterial response, we developed Domino (github.com/chris-cherry/domino), a computational tool which allows for identification of condition specific intercellular signaling patterns connected to transcription factor activation from single cell data. The Domino networks self-assembled into signaling modules and cellular subsets involved in signaling independent of clustering, defining interactions between immune, fibroblast, and tissue-specific modules with biomaterials-specific communication patterns. Further compilation and integration of biomaterials single cell data sets will delineate the impact of materials chemical and physical properties and biological factors, such as anatomical placement, age, or systemic disease, that will direct biomaterials design.

bioinformatics↗

bioRxiv2020

Synthetic Maturation of Multilineage Human Liver Organoids via Genetically Guided Engineering

Pluripotent stem cell (PSC)-derived organoids are emerging as novel human-based microphysiological models but display immature phenotypes with limited subsets of endothelial or stromal cells. Here we demonstrate that in vitro manipulation of gene regulatory networks (GRNs) in PSC-derived liver organoids selected either through computational analysis or targeted tissue design can advance tissue maturation in vitro. Through an unbiased comparison with the genetic signature of mature livers, we identify downregulated GRNs in fetal liver organoids compared to adult livers. We demonstrate that overexpression of PROX1 and ATF5, together with targeted CRISPR-based transcriptional activation of endogenous CYP3A4, drives maturation in vitro. Single cell analyses reveal hepatobiliary-, endothelial-, and stellate-like cell populations. The engineered organoids demonstrate enhanced vasculogenesis, capture native liver characteristics (e.g. FXR signaling, CYP3A4 activity), and exhibit therapeutic potential in mice. Collectively, our approach provides a genetically guided framework for engineering developmentally advanced multilineage tissues from hiPSCs. HIGHLIGHTSO_LIIn vitro tissue maturation via genetically encoded molecular programs C_LIO_LIComputational analysis to identify maturation transcription factors in liver organoids C_LIO_LIPromoting vascularization of organoids via genetically encoded molecular programs C_LIO_LISingle cell analysis of parenchymal and non-parenchymal cells C_LIO_LIModeling of native liver functions and in vivo therapeutic potential C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=168 HEIGHT=200 SRC="FIGDIR/small/087445v1_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@6ec32aorg.highwire.dtl.DTLVardef@1cf2eeforg.highwire.dtl.DTLVardef@1128748org.highwire.dtl.DTLVardef@16fb5a7_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2020

Evaluating the transcriptional fidelity of cancer models

BackgroundCancer researchers use cell lines, patient derived xenografts, engineered mice, and tumoroids as models to investigate tumor biology and to identify therapies. The generalizability and power of a model derives from the fidelity with which it represents the tumor type under investigation, however, the extent to which this is true is often unclear. The preponderance of models and the ability to readily generate new ones has created a demand for tools that can measure the extent and ways in which cancer models resemble or diverge from native tumors. MethodsWe developed a machine learning based computational tool, CancerCellNet, that measures the similarity of cancer models to 22 naturally occurring tumor types and 36 subtypes, in a platform and species agnostic manner. We applied this tool to 657 cancer cell lines, 415 patient derived xenografts, 26 distinct genetically engineered mouse models, and 131 tumoroids. We validated CancerCellNet by application to independent data, and we tested several predictions with immunofluorescence. ResultsWe have documented the cancer models with the greatest transcriptional fidelity to natural tumors, we have identified cancers underserved by adequate models, and we have found models with annotations that do not match their classification. By comparing models across modalities, we report that, on average, genetically engineered mice and tumoroids have higher transcriptional fidelity than patient derived xenografts and cell lines in four out of five tumor types. However, several patient derived xenografts and tumoroids have classification scores that are on par with native tumors, highlighting both their potential as faithful model classes and their heterogeneity. ConclusionsCancerCellNet enables the rapid assessment of transcriptional fidelity of tumor models. We have made CancerCellNet available as freely downloadable software and as a web application that can be applied to new cancer models that allows for direct comparison to the cancer models evaluated here.

bioinformatics↗

bioRxiv2019

A single cell transcriptional atlas of early synovial joint development

Synovial joint development begins with the formation of the interzone, a region of condensed mesenchymal cells at the site of the prospective joint. Recently, lineage tracing strategies have revealed that Gdf5-lineage cells native to and from outside the interzone contribute to most, if not all, of the major joint components. However, there is limited knowledge of the specific transcriptional and signaling programs that regulate interzone formation and fate diversification of synovial joint constituents. To address this, we have performed single cell RNA-Seq analysis of 6,202 synovial joint progenitor cells from the developing murine knee joint from E12.5 to E15.5. By using a combination of computational analytics, in situ hybridization, and functional analysis of prospectively isolated populations, we have inferred the underlying transcriptional networks of the major developmental paths for joint progenitors. Our freely available single cell transcriptional atlas will serve as a resource for the community to uncover transcriptional programs and cell interactions that regulate synovial joint development.

developmental biology↗

bioRxiv2019

Single cell RNA-seq in regenerative and fibrotic biomaterial environments defines new macrophage subsets

Macrophages play diverse roles in the immune response to infection, cancer, and wound healing where they respond to local environmental signals, yet identification and phenotypic characterization of functional subsets in vivo remains limited. We performed single cell RNA sequencing analysis on differentiated macrophages sorted from a biologic matrix-induced regenerative environment versus a synthetic biomaterial foreign body response (FBR), characterized by TH2/interleukin (IL)-4 and TH17/IL-17, respectively. In the regenerative environment, unbiased clustering and pseudotime analysis revealed distinct macrophage subsets responsible for antigen presentation, chemoattraction, and phagocytosis, as well as a small population with expression profiles of both dendritic cells and skeletal muscle. In the FBR environment, we identified a CD9hi+IL-36{gamma}+ macrophage subset that expressed TH17-associated molecules characteristic of certain auto-immune responses that were virtually absent in mice lacking the IL-17 receptor. Surface marker combinations including CD9 and CD301b defined macrophage fibrotic and regenerative subsets enabling functional assessment and identification in human tissue. Application of the terminal macrophage subsets to train the SingleCellNet algorithm and comparison to human and mouse macrophages in tumor, lung, and liver suggest broad relevance of macrophage classification. These distinct macrophage subsets demonstrate previously unrecognized myeloid phenotypes involved in different tissue responses and provide new targets for potential therapeutic modulation of certain pathologic states and tissue repair.

immunology↗

bioRxiv2019

Radiator: a cloud-based framework for deploying reusable bioinformatics tools

SummaryAccessibility and usability of compute-intensive bioinformatics tools can be increased with simplified web-based graphic user interfaces. However, deploying such tools as web applications presents additional barriers, including the complexity of developing a usable interface, network latency in transferring large datasets, and cost, which we encountered in developing a web-based version of our command-line tool CellNet. Learning and generalizing from this experience, we have devised a lightweight framework, Radiator, to facilitate deploying bioinformatics tools as web applications. To achieve reproducibility, usability, consistent accessibility, throughput, and cost-efficiency, Radiator is designed to be deployed on the cloud. Here, we describe the internals of Radiator and how to use it.\n\nAvailability and ImplementationCode for Radiator and the CellNet Web Application are freely available at https://github.com/pcahan1 under the MIT license. The CellNet WebApp, Radiator, and Radiator-derived applications can be launched through public Amazon Machine Images from the cloud provider Amazon Web Services (AWS) (https://aws.amazon.com/).

bioinformatics↗

bioRxiv2018

SingleCellNet: a computational tool to classify single cell RNA-Seq data across platforms and across species

Single cell RNA-Seq has emerged as a powerful tool in diverse applications, ranging from determining the cell-type composition of tissues to uncovering the regulators of developmental programs. A near-universal step in the analysis of single cell RNA-Seq data is to hypothesize the identity of each cell. Often, this is achieved by finding cells that express combinations of marker genes that had previously been implicated as being cell-type specific, an approach that is not quantitative and does not explicitly take advantage of other single cell RNA-Seq studies. Here, we describe our tool, SingleCellNet, which addresses these issues and enables the classification of query single cell RNA-Seq data in comparison to reference single cell RNA-Seq data. SingleCellNet compares favorably to other methods, and it is notably able to make sensitive and accurate classifications across platforms and species. We demonstrate how SingleCellNet can be used to classify previously undetermined cells, and how it can be used to assess the outcome of cell fate engineering experiments.\n\nHighlightO_LISingleCellNet (SCN) enables the classification of scRNA-Seq data across platforms and species\nC_LIO_LISCN is open source and extendible\nC_LIO_LIWe illustrate the utility of SCN with three example applications\nC_LI\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=161 SRC=\"FIGDIR/small/508085_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (47K):\norg.highwire.dtl.DTLVardef@1750006org.highwire.dtl.DTLVardef@548dbdorg.highwire.dtl.DTLVardef@1257cb1org.highwire.dtl.DTLVardef@1d8072_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2018

Short hairpin RNAs artifactually impair cell growth and suppress clustered microRNA expression.

Functional gene disruption is a central tenet of cancer research, where novel drug targets are often identified and validated through cell-growth based knockdown studies or screens. Short hairpin RNA (shRNA)-mediated mRNA knockdown is widely used in both academic and pharmaceutical settings. However, off-target effects of shRNAs as well as interference with endogenous small RNA processing have been reported. We show here that lentiviral delivery of both gene-specific and non-targeting control shRNAs impair in vitro cell growth in a sequence independent manner. In addition, exogenous shRNAs induce a depressed cell-cycle-gene expression signature that is also shRNA-sequence independent and present across several studies. Further, we observe an shRNA mediated general repression of microRNAs belonging to polycistronic genetic clusters, including microRNAs from established oncogenic microRNA clusters. The collective impact of these observations is particularly relevant for cancer research, given the widespread historical use of shRNAs and the common goal of interrogating genes that regulate proliferation. We therefore recommend that when employing shRNA for target validation, care be taken to titrate shRNA dose, use hairpin-expressing controls, perform gene-of-interest rescue experiments and/or corroborate shRNA-derived results by small interfering RNA (siRNA) knockdown or CRISPR/Cas9-mediated genetic knockout. Minimizing these deleterious sequence independent effects will improve research fidelity and help address reported challenges in experimental reproducibility.

cancer biology↗