Biology subjects

Kaminski, N.

Publications and source records attributed to Kaminski, N..

At least 55 records · Page 3Linked to original sources

bioRxiv2022

iDESC: Identifying differential expression in single-cell RNA sequencing data with multiple subjects

Single-cell RNA sequencing (scRNA-seq) enables assessment of transcriptome-wide changes at single-cell resolution. However, dominant subject effect in scRNA-seq datasets with multiple subjects severely confounds cell-type-specific differential expression (DE) analysis. We developed iDESC to separate subject effect from disease effect with consideration of dropouts to identify DE genes. iDESC was shown to have well-controlled type I error and high power compared to existing methods and obtained the best consistency between datasets and disease relevance in two scRNA-seq datasets from same disease, suggesting the importance of considering subject effect and dropouts in the DE analysis of scRNA-seq data with multiple subjects.

bioinformatics↗

bioRxiv2022

Comprehensive visualization of cell-cell interactions in single-cell and spatial transcriptomics with NICHES

SummaryRecent years have seen the release of several toolsets that reveal cell-cell interactions from single-cell data. However, all existing approaches leverage mean celltype gene expression values, and do not preserve the single-cell fidelity of the original data. Here, we present NICHES (Niche Interactions and Communication Heterogeneity in Extracellular Signaling), a tool to explore extracellular signaling at the truly single-cell level. NICHES allows embedding of ligand-receptor signal proxies to visualize heterogeneous signaling archetypes within cell clusters, between cell clusters, and across experimental conditions. When applied to spatial transcriptomic data, NICHES can be used to reflect local cellular microenvironment. NICHES can operate with any list of ligand-receptor signaling mechanisms and is compatible with existing single-cell packages and pseudotime techniques. NICHES is also a user friendly and extensible program, allowing rapid analysis of cell-cell signaling at single-cell resolution. Availability and implementationNICHES is an open-source software implemented in R under academic free license v3.0 and it is available at github.com/msraredon/NICHES. Use-case vignettes are available at https://msraredon.github.io/NICHES/. Contactmichasam.raredon@yale.edu; yuval.kluger@yale.edu

bioinformatics↗

bioRxiv2022

Saracatinib, a Selective Src Kinase Inhibitor, Blocks Fibrotic Responses in In Vitro, In Vivo and Ex Vivo Models of Pulmonary Fibrosis

Idiopathic Pulmonary Fibrosis (IPF) is a chronic, progressive, and often fatal disorder. Two FDA approved anti-fibrotic drugs, nintedanib and pirfenidone, slow the rate of decline in lung function, but responses are variable and side effects are common. Using an in-silico data-driven approach, we identified a robust connection between the transcriptomic perturbations in IPF disease and those induced by saracatinib, a selective Src kinase inhibitor, originally developed for oncological indications. Based on these observations, we hypothesized that saracatinib would be effective at attenuating pulmonary fibrosis. We investigated the anti-fibrotic efficacy of saracatinib relative to nintedanib and pirfenidone in three preclinical models: (i) in vitro in normal human lung fibroblasts (NHLFs); (ii) in vivo in bleomycin and recombinant adenovirus transforming growth factor-beta (Ad-TGF-{beta}) murine models of pulmonary fibrosis; and (iii) ex vivo in precision cut lung slices from these mouse models. In each model, the effectiveness of saracatinib in blocking fibrogenic responses was equal or superior to nintedanib and pirfenidone.

systems biology↗

bioRxiv2021

A lung targeted miR-29 Mimic as a Therapy for Pulmonary Fibrosis

microRNAs are non-coding RNAs that negatively regulate gene networks. Previously, we reported a systemically delivered miR-29 mimic MRG-201 that reduced fibrosis in animal models, but at doses prohibiting clinical translation. Here, we generated MRG-229, a next-gen miR-29 mimic with improved chemical stability, conjugated with the internalization moiety BiPPB (PDGFbetaR-specific bicyclic peptide). In TGF-b-treated human lung fibroblasts and precision cut lung slices, MRG-229 decreased COL1A1 and ACTA2 gene expression and reduced collagen production. In bleomycin-treated mice, intravenous or subcutaneous delivery of MRG-229 downregulated profibrotic gene programs at doses more than ten-fold lower than the original compound. In rats and non-human primates, and at clinically relevant doses, MRG-229 was well tolerated, with no adverse findings observed. In human peripheral blood decreased mir-29 concentrations were associated with increased mortality in two cohorts potentially identified as a target population for treatment. Collectively, our results provide support for the development of MRG-229 as a potential therapy in humans with IPF. One Sentence SummaryOne Sentence Summary: A stabilized, next-generation miR-29 mimic has been developed that demonstrates efficacy at commercially viable doses with a robust safety margin in non-human primates.

molecular biology↗

bioRxiv2021

ProtAnno, an Automated Cell Type Annotation Tool for Single Cell Proteomics Data that Integrates Information from Multiple Reference Sources

Compared with sequencing-based global genomic profiling, cytometry labels targeted surface markers on millions of cells in parallel either by conjugated rare earth metal particles or Unique Molecular Identifier (UMI) barcodes. Correct annotation of these cells to specific cell types is a key step in the analysis of these data. However, there is no computational tool that automatically annotates single cell proteomics data for cell type inference. In this manuscript, we propose an automated single cell proteomics data annotation approach called ProtAnno to facilitate cell type assignments without laborious manual gating. ProtAnno is designed to incorporate information from annotated single cell RNA-seq (scRNA-seq), CITE-seq, and prior data knowledge (which can be imprecise) on biomarkers for different cell types. We have performed extensive simulations to demonstrate the accuracy and robustness of ProtAnno. For several single cell proteomics datasets that have been manually labeled, ProtAnno was able to correctly label most single cells. In summary, ProtAnno offers an accurate and robust tool to automate cell type annotations for large single cell proteomics datasets, and the analysis of such annotated cell types can offer valuable biological insights.

bioinformatics↗

bioRxiv2021

High CXCL6 drives matrix expression and correlate with markers of poor outcome in IPF

Signaling via G protein-coupled receptors (GPCRs) can modulate levels of cyclic adenosine monophosphate (cAMP) and shape the functions of fibroblasts in idiopathic pulmonary fibrosis (IPF). We have identified Chemokine (C-X-C) Motif Ligand 6 (CXCL6) as a potential pro-fibrotic GPCR ligand. We tested the function of CXCL6 in ex vivo human donor and fibrotic lung fibroblasts and in an animal model of pulmonary fibrosis. We also measured levels of CXCL6 in the blood and bronchoalveolar lavage (BAL) of patients with IPF. CXCL6 decreased cAMP levels in a dose-dependent manner in Donor and IPF Fibroblasts. CXCL6 mRNA and protein were localized to epithelial cells. Administration of mCXCL5 (LIX, murine CXCL6 homologue) to mice increased collagen synthesis with and without bleomycin. CXCL6 increased Collagen I and -SMA levels in Donor and IPF Fibroblasts. Silencing of CXCR1/2 as well as Reparixin, a CXCR1/2 inhibitor, blocked effects of CXCL6. Treprostinil blocked effects of CXCL6 only on levels of -SMA but not on Collagen I. CXCL6 levels in the BAL of two separate cohorts of patients with IPF was associated with poor survival. We conclude that high CXCL6 drives fibroblast function and correlates with poor outcomes in IPF.

cell biology↗

bioRxiv2021

Pseudohypoxic HIF pathway activation dysregulates collagen structure-function in human lung fibrosis

Extracellular matrix (ECM) stiffening with downstream activation of mechanosensitive pathways is strongly implicated in fibrosis. We previously reported that altered collagen nanoarchitecture is a key determinant of pathogenetic ECM structure-function in human fibrosis (Jones et al., 2018). Here, through human tissue, bioinformatic and ex vivo studies we show that hypoxia-inducible factor (HIF) pathway activation is a critical pathway for this process regardless of oxygen status (pseudohypoxia). Whilst TGF{beta} increased rate of fibrillar collagen synthesis, HIF pathway activation was required to dysregulate post-translational modification of fibrillar collagen, promoting bone-type cross-linking, altering collagen nanostructure, and increasing tissue stiffness. In vitro, knock down of Factor Inhibiting HIF (FIH) or oxidative stress caused pseudohypoxic HIF activation in normal fibroblasts. In contrast, endogenous FIH activity was reduced in fibroblasts from patients with lung fibrosis in association with significantly increased normoxic HIF pathway activation. In human lung fibrosis tissue, HIF mediated signalling was increased at sites of active fibrogenesis whilst subpopulations of IPF lung mesenchymal cells had increases in both HIF and oxidative stress scores. Our data demonstrate that oxidative stress can drive pseudohypoxic HIF pathway activation which is a critical regulator of pathogenetic collagen structure-function in fibrosis.

cell biology↗

bioRxiv2021

CINS: Cell Interaction Network inference from Single cell expression data

Studies comparing single cell RNA-Seq (scRNA-Seq) data between conditions mainly focus on differences in the proportion of cell types or on differentially expressed genes. In many cases these differences are driven by changes in cell interactions which are challenging to infer without spatial information. To determine cell-cell interactions that differ between conditions we developed the Cell Interaction Network Inference (CINS) pipeline. CINS combines Bayesian network analysis with regression-based modeling to identify differential cell type interactions and the proteins that underlie them. We tested CINS on a disease case control and on an aging human dataset. In both cases CINS correctly identifies cell type interactions and the ligands involved in these interactions. We performed additional mouse aging scRNA-Seq experiments which further support the interactions identified by CINS.

bioinformatics↗

bioRxiv2021

Connectome: computation and visualization of cell-cell signaling topologies in single-cell systems data

Single-cell RNA-sequencing data can revolutionize our understanding of the patterns of cell-cell and ligand-receptor connectivity that influence the function of tissues and organs. However, the quantification and visualization of these patterns are major computational and epistemological challenges. Here, we present Connectome, a software package for R which facilitates rapid calculation, and interactive exploration, of cell-cell signaling network topologies contained in single-cell RNA-sequencing data. Connectome can be used with any reference set of known ligand-receptor mechanisms. It has built-in functionality to facilitate differential and comparative connectomics, in which complete mechanistic networks are quantitatively compared between systems. Connectome includes computational and graphical tools designed to analyze and explore cell-cell connectivity patterns across disparate single-cell datasets. We present approaches to quantify these topologies and discuss some of the biologic theory leading to their design.

systems biology↗

bioRxiv2021

Single Cell RNA-seq and Mass Cytometry Reveals a Novel and a Targetable Population of Macrophages in Idiopathic Pulmonary Fibrosis

In this study, we leveraged a combination of single cell RNAseq, cytometry by time of flight (CyTOF), and flow cytometry to study the biology of a unique macrophage population in pulmonary fibrosis. Using the profiling data from 312,928 cells derived from 32 idiopathic pulmonary fibrosis (IPF), 29 healthy control and 18 chronic obstructive pulmonary disease (COPD) lungs, we identified an expanded population of macrophages in IPF that have a unique transcriptional profile associated with pro-fibrotic signature. These macrophages attain a hybrid transitional state between alveolar and interstitial macrophages, are enriched with biological processes of pro-fibrotic immune cells, and express novel surface markers and genes that have not been previously reported. We then applied single cell CyTOF to simultaneously measure 37 markers to precisely phenotype the uniquely expanded macrophage subset in IPF lungs. The SPADE algorithm independently identified an expanded macrophage cluster, and validated CD84 and CD36 as novel surface markers that highly label this cluster. Using a separate validation cohort, we confirmed an increase in CD84++CD36++ macrophage population in IPF compared to control and COPD lungs by flow cytometry. Further, using the signature from the IPF-specific macrophages and the LINCS drug database, we predicted small molecules that could reverse the signature of IPF-specific macrophages, and validated two molecules, CRT and Cucur, using THP-1 derived human macrophages and precision-cut lung slices (PCLS) from IPF patients. Utilizing a multi-dimensional translational approach, our work identified a novel and targetable population of macrophages found in end-stage pulmonary fibrosis. One Sentence SummarySingle cell RNAseq, CyTOF, and flow cytometry reveal the presence of an aberrant macrophage population in pulmonary fibrosis

cell biology↗

bioRxiv2020

A Markov Random Field Model for Network-based Differential Expression Analysis of Single-cell RNA-seq Data

BackgroundRecent development of single cell sequencing technologies has made it possible to identify genes with different expression (DE) levels at the cell type level between different groups of samples. In this article, we propose to borrow information through known biological networks to increase statistical power to identify differentially expressed genes (DEGs). ResultsWe develop MRFscRNAseq, which is based on a Markov Random Field (MRF) model to appropriately accommodate gene network information as well as dependencies among cell types to identify cell-type specific DEGs. We implement an Expectation-Maximization (EM) algorithm with mean field-like approximation to estimate model parameters and a Gibbs sampler to infer DE status. Simulation study shows that our method has better power to detect cell-type specific DEGs than conventional methods while appropriately controlling type I error rate. The usefulness of our method is demonstrated through its application to study the pathogenesis and biological processes of idiopathic pulmonary fibrosis (IPF) using a single-cell RNA-sequencing (scRNA-seq) data set, which contains 18,150 protein-coding genes across 38 cell types on lung tissues from 32 IPF patients and 28 normal controls. ConclusionsThe proposed MRF model is implemented in the R package MRFscRNAseq available on GitHub. By utilizing gene-gene and cell-cell networks, our method increases statistical power to detect differentially expressed genes from scRNA-seq data.

bioinformatics↗

bioRxiv2020

Type I Interferon Transcriptional Network Regulates Expression of Coinhibitory Receptors in Human T cells

While inhibition of T cell co-inhibitory receptors has revolutionized cancer therapy, the mechanisms governing their expression on human T cells have not been elucidated. Type 1 interferon (IFN-I) modulates T cell immunity in viral infection, autoimmunity, and cancer, and may facilitate induction of T cell exhaustion in chronic viral infection1,2. Here we show that IFN-I regulates co-inhibitory receptors expression on human T cells, inducing PD-1/TIM-3/LAG-3 while surprisingly inhibiting TIGIT expression. High-temporal-resolution mRNA profiling of IFN-I responses enabled the construction of dynamic transcriptional regulatory networks uncovering three temporal transcriptional waves. Perturbation of key transcription factors on human primary T cells revealed both canonical and non-canonical IFN-I transcriptional regulators, and identified unique regulators that control expression of co-inhibitory receptors. To provide direct in vivo evidence for the role of IFN-I on co-inhibitory receptors, we then performed single cell RNA-sequencing in subjects infected with SARS-CoV-2, where viral load was strongly associated with T cell IFN-I signatures. We found that the dynamic IFN-I response in vitro closely mirrored T cell features with acute IFN-I linked viral infection, with high LAG3 and decreased TIGIT expression. Finally, our gene regulatory network identified SP140 as a key regulator for differential LAG3 and TIGIT expression. The construction of co-inhibitory regulatory networks induced by IFN-I with identification of unique transcription factors controlling their expression may provide targets for enhancement of immunotherapy in cancer, infectious diseases, and autoimmunity.

immunology↗

bioRxiv2020

Integrated Single Cell Atlas of Endothelial Cells of the Human Lung

BackgroundDespite its importance in health and disease, the cellular diversity of the lung endothelium has not been systematically characterized in humans. Here we provide a reference atlas of human lung endothelial cells (ECs), to facilitate a better understanding of the phenotypic diversity and composition of cells comprising the lung endothelium, both in health and disease. MethodsWe reprocessed control single cell RNA sequencing (scRNAseq) data from five datasets of whole lungs that were used for the analysis of pan-endothelial markers, we later included a sixth dataset of sorted control EC for the vascular subpopulation analysis. EC populations were characterized through iterative clustering with subsequent differential expression analysis. Marker genes were validated by immunohistochemistry and in situ hybridization. Signaling network between different lung cell types was studied using connectomic analysis. For cross species analysis we applied the same methods to scRNAseq data obtained from mouse lungs. ResultsThe six lung scRNAseq datasets were reanalyzed and annotated to identify over 15,000 vascular EC cells from 73 individuals. Differential expression analysis of EC revealed signatures corresponding to endothelial lineage, including pan-endothelial, pan-vascular and subpopulation-specific marker gene sets. Beyond the broad cellular categories of lymphatic, capillary, arterial and venous ECs we found previously indistinguishable subpopulations; among venous EC we identified two previously indistinguishable populations, pulmonary-venous ECs (COL15A1neg) localized to the lung parenchyma and systemic-venous ECs (COL15A1pos) localized to the airways and the visceral pleura; among capillary EC we confirmed their subclassification into recently discovered aerocytes characterized by EDNRB, SOSTDC1 and TBX2 and general capillary EC. We confirmed that all six endothelial cell types, including the systemic-venous EC and aerocytes are present in mice and identified endothelial marker genes conserved in humans and mice. Ligand-Receptor connectome analysis revealed important homeostatic crosstalk of EC with other lung resident cell types. Our manuscript is accompanied by an online data mining tool (www.LungEndothelialCellAtlas.com). ConclusionOur integrated analysis provides the comprehensive and well-crafted reference atlas of lung endothelial cells in the normal lung and confirms and describes in detail previously unrecognized endothelial populations across a large number of humans and mice.

cell biology↗

bioRxiv2020

Single-cell RNA-sequencing reveals dysregulation of molecular programs associated with SARS-CoV-2 severity and outcomes in patients with chronic lung disease

Patients with chronic lung disease (CLD) have an increased risk for severe coronavirus disease-19 (COVID-19) and poor outcomes. Here, we analyzed the transcriptomes of 605,904 single cells isolated from healthy and CLD lungs to identify molecular characteristics of lung cells that may account for worse COVID-19 outcomes in patients with chronic lung diseases. We observed a similar cellular distribution and relative expression of SARS-CoV-2 entry factors in control and CLD lungs. CLD epithelial cells expressed higher levels of genes linked directly to the efficiency of viral replication and innate immune response. Additionally, we identified basal differences in inflammatory gene expression programs that highlight how CLD alters the inflammatory microenvironment encountered upon viral exposure to the peripheral lung. Our study indicates that CLD is accompanied by changes in cell-type-specific gene expression programs that prime the lung epithelium for and influence the innate and adaptive immune responses to SARS-CoV-2 infection.

cell biology↗

bioRxiv2020

Airway Basal Cells show a dedifferentiated KRT17high Phenotype and promote Fibrosis in Idiopathic Pulmonary Fibrosis

Idiopathic pulmonary fibrosis (IPF) is a fatal disease with limited treatment options. In this study we focus on the profibrotic properties of airway basal cells (ABC) obtained from patients with IPF (IPF-ABC). Single cell RNA sequencing of bronchial brushes revealed extensive reprogramming of IPF-ABC towards a KRT17high PTENlow dedifferentiated cell type. In the 3D organoid model, compared to ABC obtained from healthy volunteers, IPF-ABC give rise to more bronchospheres, de novo bronchial structures resembling lung developmental processes, induce fibroblast proliferation and extracellular matrix deposition in co-culture. Intratracheal application of IPF-ABC into minimally injured lungs of Rag2-/- or NRG mice causes severe fibrosis, remodeling of the alveolar compartment, and formation of honeycomb cyst-like structures. Connectivity MAP analysis of scRNA seq of bronchial brushings suggested that gene expression changes in IPF-ABC can be reversed by SRC inhibition. After demonstrating enhanced SRC expression and activity in these cells, and in IPF lungs, we tested the effects of saracatinib, a potent SRC inhibitor previously studied in humans. We demonstrated that saracatinib modified in-vitro and in-vivo the profibrotic changes observed in our 3D culture system and novel mouse xenograft model.

cell biology↗

bioRxiv2020

Decreased miR-24-3p potentiates DNA damage responses and increases susceptibility to COPD

Activation of the DNA damage response (DDR) due to chronic exposure to cigarette smoke (CS) is implicated in the pathogenesis of Chronic Obstructive Pulmonary Disease (COPD). However, not all smokers develop COPD and the pathologic consequences of CS exposure are heterogenous. Cellular mechanisms that regulate the DDR and contribute to disease progression in susceptible individuals are poorly understood. Because microRNAs are well known regulators of the DDR, we evaluated microRNA expression arrays performed on lung samples from 172 subjects with and without COPD. We identified miR-24-3p as the microRNA best correlated with radiographic emphysema ({rho}=-0.353, P=1.3e-04) and validated this finding in multiple cohorts. In a CS-exposure mouse model, miR-24-3p inhibition increased emphysema severity. In human airway epithelial cells, miR-24-3p suppressed apoptosis through the BH3-only protein BIM and suppressed homology-directed DNA repair and the DNA repair protein BRCA1. Finally, we found BIM and BRCA1 were increased in COPD lung tissue and inversely correlated with miR-24-3p expression. We concluded that decreased miR-24-3p expression increases COPD susceptibility and potentiates the DDR through BIM and BRCA1.

cell biology↗

bioRxiv2019

Single-cell time-series mapping of cell fate trajectories reveals an expanded developmental potential for human PSC-derived distal lung progenitors

Alveolar epithelial type 2 cells (AEC2s) are the facultative progenitors responsible for maintaining lung alveoli throughout life, yet are difficult to access from patients for biomedical research or lung regeneration applications. Here we engineer AEC2s from human induced pluripotent stem cells (iPSCs) in vitro and use single cell RNA sequencing (scRNA-seq) to profile the detailed kinetics of their differentiation over time. We focus on both the desired target cells as well as those that appear to diverge to alternative endodermal fates. By combining scRNA-seq with lentiviral barcoding to trace differentiating clones, we reveal the bifurcating cell fate trajectories followed as primordial lung progenitors differentiate into mature AEC2s. We define the global transcriptomic signatures of primary developing human AEC2s from fetal through adult stages in order to identify the subset of in vitro differentiating cells that appear to recapitulate the path of in vivo development. In addition, we develop computational methods based on Continuous State Hidden Markov Models (CSHMM) to identify the precise timing and type of signals, such as over-exuberant Wnt responses, that induce some early multipotent NKX2-1+ progenitors to lose lung fate as they clonally diverge into a variety of non-lung endodermal lineages. Finally, we find that this initial developmental plasticity is regulatable via Wnt modulation, and subsides over time, ultimately resulting in iPSC-derived AEC2s that exhibit a stable phenotype and nearly limitless self-renewal capacity in vitro. Our methods and computational approaches can be widely applied to study and control directed differentiation, producing an inexhaustible supply of mature lineages, exemplified here by the generation of AEC2s.

developmental biology↗

bioRxiv2019

Single Cell RNA-seq reveals ectopic and aberrant lung resident cell populations in Idiopathic Pulmonary Fibrosis

We provide a single cell atlas of Idiopathic Pulmonary Fibrosis (IPF), a fatal interstitial lung disease, focusing on resident lung cell populations. By profiling 312,928 cells from 32 IPF, 29 healthy control and 18 chronic obstructive pulmonary disease (COPD) lungs, we demonstrate that IPF is characterized by changes in discrete subpopulations of cells in the three major parenchymal compartments: the epithelium, endothelium and stroma. Among epithelial cells, we identify a novel population of IPF enriched aberrant basaloid cells that co-express basal epithelial markers, mesenchymal markers, senescence markers, developmental transcription factors and are located at the edge of myofibroblast foci in the IPF lung. Among vascular endothelial cells in the in IPF lung parenchyma we identify an expanded cell population transcriptomically identical to vascular endothelial cells normally restricted to the bronchial circulation. We confirm the presence of both populations by immunohistochemistry and independent datasets. Among stromal cells we identify fibroblasts and myofibroblasts in both control and IPF lungs and leverage manifold-based algorithms diffusion maps and diffusion pseudotime to infer the origins of the activated IPF myofibroblast. Our work provides a comprehensive catalogue of the aberrant cellular transcriptional programs in IPF, demonstrates a new framework for analyzing complex disease with scRNAseq, and provides the largest lung disease single-cell atlas to date.

genomics↗