Biology subjects

Fertig, E.

Publications and source records attributed to Fertig, E..

12 recordsLinked to original sources

bioRxiv2026

Spatially resolved transcriptional programs link fallopian tube precursor lesions to immune activation and stromal reorganization

Despite its name, high-grade serous ovarian carcinoma (HGSC) originates in the fallopian tube, not the ovary, arising from a morphologically recognizable precursor lesion, serous tubal intraepithelial carcinoma (STIC). Yet the early cellular and microenvironmental changes driving this transformation remain poorly understood, limiting progress in early detection, interception, and prevention. Here, we generated a Visium HD spatial transcriptomic atlas of fallopian tube carcinogenesis spanning histologically unremarkable fallopian tube epithelium (FTE), STIC, and invasive HGSC. This approach enabled unbiased, tissue-wide, whole-transcriptome mapping at single-cell-level resolution within preserved histologic architecture, providing spatial granularity beyond prior region-of-interest-based platforms. STIC lesions displayed a coordinated epithelial transformation program marked by proliferation, replication stress, DNA repair activation, chromatin remodeling, and induction of tumor-associated antigens, including PRAME and CLDN6, which are emerging targets for vaccine and antigen-directed therapeutic strategies. In contrast, histologically unremarkable FTE contained spatially restricted epithelial defense programs marked by SCGB1A1 and MUC6, suggesting localized protective states that may influence susceptibility to malignant transformation. Using distance- and density-aware spatial analyses, we found that precursor lesions were embedded within immune-enriched, stromal-depleted microenvironments characterized by interferon-dominant immune activation, attenuation of TNF/NF-{kappa}B signaling, macrophage and lymphoid remodeling, and extracellular matrix-associated fibroblast interactions. Computational pathology analysis of collagen architecture confirmed reduced collagen fiber density in STIC-adjacent stroma, linking transcriptomic evidence of stromal remodeling to structural extracellular matrix changes. Together, these data define early epithelial, immune, and stromal programs associated with STIC and identify tumor-associated antigens, epithelial defense states, and immune-stromal niches as candidate targets for HGSC prevention and early interception.

cancer biology↗

bioRxiv2026

Clear cell renal cell carcinoma consensus transcriptomic programs reveal converging trajectories towards aggressive disease

Clear cell renal cell carcinoma (ccRCC) is characterized by a branching genomic trajectory in which early biallelic VHL inactivation splits into PBRM1- and BAP1-mutant lineages. However, driver mutations alone do not account for the molecular and phenotypic heterogeneity. To dissect this heterogeneity, we developed a non-negative matrix factorization (NMF)-based gene expression analytical framework for identifying recurrent transcriptional programs across factorization dimensionalities and across datasets. We applied it to three curated datasets (IMmotion151, n = 823; JAVELIN Renal 101, n = 726; TCGA, n = 614) to define 17 consensus transcriptomic programs (CTPs). Mapping CTPs onto single-cell RNAseq (scRNAseq) of human ccRCC tumors and patient-derived tumorgraft models linked these programs to their cellular sources, distinguishing RCC-intrinsic, tumor-cell-extrinsic, and mixed programs. RCC-intrinsic CTPs associated with canonical drivers, including VHL (R1), PBRM1 (R2), BAP1 (R4), PTEN/TSC1 (R3), TFE3/TFEB fusions (R5), NF2 (R6), and CDKN2A/TP53 (MP-Prolif). Additional CTPs captured tumor microenvironment (TME) composition (TME-Tcell, TME-Myelo, TME-Endo, TME-Stroma) and biological processes active across multiple cellular compartments, including proliferation, Y-chromosome-linked expression in male tumors, ciliary biology, and translation. Trajectory inference methods revealed PBRM1-like and BAP1-like branches that converged on a shared aggressive late transcriptomic stage (TS) associated with higher nuclear grade, additional driver alterations, myeloid/stromal infiltration, and poor clinical outcomes. Spatial transcriptomics (and multiregional sequencing) of paired conventional ccRCC and sarcomatoid regions linked TS advancement with morphological progression and clonal evolution. After adjusting for stage, grade, and BAP1/PBRM1 status, TS remained independently prognostic. Furthermore, our data suggest that immune checkpoint inhibitor combinations are particularly beneficial for MP-Prolif and not R1 utilizing specimens. In summary, we present an atlas of recurring transcriptomic programs in RCC and an ontological framework bridging genotype, tumor-cell-intrinsic gene expression, and microenvironment remodeling, with implications for risk stratification and treatment selection in ccRCC.

Cancer Biology↗

bioRxiv2025

Inferring the regulation dynamics of oscillatorynetworks from scRNA-seq data

Oscillatory processes such as the cell cycle play critical roles in cell fate determination and disease development. Yet, most current gene regulatory network (GRN) inference methods are based on gene-gene correlations or temporal progression, not adequately accounting for the recurrence in cyclic processes. We hypothesize that constraining the continuous ordering of relative positions along the cell cycle can enhance GRN inference accuracy of cell cycle regulation. To test performance, we evaluated eight representative methods and applied three of them to a mouse retinal progenitor single-cell gene expression dataset [1]. Incorporating cell cycle positions inferred by Tricycle [2] led to significant improvements compared against using experimental times, particularly for early progenitor cells that been hypothesized to be more intrinsically driven by cell cycle regulation. These findings highlight the promise of integrating oscillatory processes into causal inference frameworks to advance our understanding of gene regulation.

bioinformatics↗

bioRxiv2021

scatterHatch: an R/Bioconductor package for colorblind accessible visualization of single-cell data

Reduced-dimension or spatial in situ scatter plots are widely employed in bioinformatics papers analyzing single-cell data to present phenomena or cell-conditions of interest in cell groups. When displaying these cell groups, color is frequently the only graphical cue used to differentiate them. However, as the complexity of the information presented in these visualizations increases, the usefulness of color as the only visual cue declines, especially for the sizable readership with color-vision deficiencies (CVDs). In this paper, we present scatterHatch, an R package that creates easily interpretable scatter plots by redundant coding of cell groups using colors as well as patterns. We give examples to demonstrate how the scatterHatch plots are more accessible than simple scatter plots when simulated for various types of CVDs.

bioinformatics↗

bioRxiv2021

Mapping origins of variation in neural trajectories of human pluripotent stem cells

Variability between human pluripotent stem cell (hPSC) lines remains a challenge and opportunity in biomedicine. We identified differences in the early lineage emergence across hPSC lines that mapped on the antero-posterior axis of embryonic development. RNA-seq analysis revealed dynamic transcriptomic patterns that defined the emergence of mesendodermal versus neuroectodermal lineages conserved across hPSC lines and cell line-specific transcriptional signatures that were invariant across differentiation. The stable cell line-specific transcriptomic patterns predicted the retinoic acid (RA) response of the cell lines, resulting in distinct bias towards fore-versus hind-brain fates. Replicate hPSC lines and paired adult donor tissue demonstrated that cells from individual humans expressed unique and long-lasting transcriptomic signatures associated with evolutionarily recent genes. In addition to this genetic contribution, we found that replicate lines from a single donor showed divergent brain regional fates linked to distinct chromatin states, indicating that epigenetic mechanisms also contribute to neural fate differences. This variation in lineage bias and its correlation with RA responsive gene expression was also observed in a large collection of hPSC lines. These results define transcriptomic differences in hPSCs that initiate a critical early step specifying anterior or posterior neural fates.

neuroscience↗

bioRxiv2019

projectR: An R/Bioconductor package for transfer learning via PCA, NMF, correlation, and clustering

MotivationDimension reduction techniques are widely used to interpret high-dimensional biological data. Features learned from these methods are used to discover both technical artifacts and novel biological phenomena. Such feature discovery is critically import to large single-cell datasets, where lack of a ground truth limits validation and interpretation. Transfer learning (TL) can be used to relate the features learned from one source dataset to a new target dataset to perform biologically-driven validation by evaluating their use in or association with additional sample annotations in that independent target dataset.\n\nResultsWe developed an R/Bioconductor package, projectR, to perform TL for analyses of genomics data via TL of clustering, correlation, and factorization methods. We then demonstrate the utility TL for integrated data analysis with an example for spatial single-cell analysis.\n\nAvailabilityprojectR is available on Bioconductor and at https://github.com/genesofeve/projectR.\n\nContactgsteinobrien@jhmi.edu; ejfertig@jhmi.edu

bioinformatics↗

bioRxiv2019

CoGAPS 3: Bayesian non-negative matrix factorization for single-cell analysis with asynchronous updates and sparse data structures

MotivationBayesian factorization methods, including Coordinated Gene Activity in Pattern Sets (CoGAPS), are emerging as powerful analysis tools for single cell data. However, these methods have greater computational costs than their gradient-based counterparts. These costs are often prohibitive for analysis of large single-cell datasets. Many such methods can be run in parallel which enables this limitation to be overcome by running on more powerful hardware. However, the constraints imposed by the prior distributions in CoGAPS limit the applicability of parallelization methods to enhance computational efficiency for single-cell analysis.\n\nResultsWe upgraded CoGAPS in Version 3 to overcome the computational limitations of Bayesian matrix factorization for single cell data analysis. This software includes a new parallelization framework that is designed around the sequential updating steps of the algorithm to enhance computational efficiency. These algorithmic advances were coupled with new software architecture and sparse data structures to reduce the memory overhead for single-cell data. Altogether, these updates to CoGAPS enhance the efficiency of the algorithm so that it can analyze 1000 times more cells, enabling factorization of large single-cell data sets.\n\nAvailabilityCoGAPS is available as a Bioconductor package and the source code is provided at github.com/FertigLab/CoGAPS. All efficiency updates to enable single-cell analysis available as of version 3.2.\n\nContactejfertig@jhmi.edu

bioinformatics↗

bioRxiv2018

Comprehensive analysis of retinal development at single cell resolution identifies NFI factors as essential for mitotic exit and specification of late-born cells.

Precise temporal control of gene expression in neuronal progenitors is necessary for correct regulation of neurogenesis and cell fate specification. However, the extensive cellular heterogeneity of the developing CNS has posed a major obstacle to identifying the gene regulatory networks that control these processes. To address this, we used single cell RNA-sequencing to profile ten developmental stages encompassing the full course of retinal neurogenesis. This allowed us to comprehensively characterize changes in gene expression that occur during initiation of neurogenesis, changes in developmental competence, and specification and differentiation of each of the major retinal cell types. These data identify transitions in gene expression between early and late-stage retinal progenitors, as well as a classification of neurogenic progenitors. We identify here the NFI family of transcription factors (Nfia, Nfib, and Nfix) as genes with enriched expression within late RPCs, and show they are regulators of bipolar interneuron and Muller glia specification and the control of proliferative quiescence.

developmental biology↗

bioRxiv2018

Combining mathematical and statistical modeling to simulate time course bulk and single cell gene expression data in cancer with CancerInSilico

Bioinformatics techniques to analyze time course bulk and single cell omics data are advancing. The absence of a known ground truth of the dynamics of molecular changes challenges benchmarking their performance on real data. Realistic simulated time-course datasets are essential to assess the performance of time course bioinformatics algorithms. We develop an R/Bioconductor package, CancerInSilico, to simulate bulk and single cell transcriptional data from a known ground truth obtained from mathematical models of cellular systems. This package contains a general R infrastructure for running cell-based models and simulating gene expression data based on the model states. We show how to use this package to simulate a gene expression data set and consequently benchmark analysis methods on this data set with a known ground truth. The package is freely available via Bioconductor: http://bioconductor.org/packages/CancerInSilico/

systems biology↗

bioRxiv2017

Enter the matrix: Interpreting unsupervised feature learning with matrix decomposition to discover hidden knowledge in high-throughput omics data

Omics data contains signal from the molecular, physical, and kinetic inter- and intra-cellular interactions that control biological systems. Matrix factorization techniques can reveal low-dimensional structure from high-dimensional data that reflect these interactions. These techniques can uncover new biological knowledge from diverse high-throughput omics data in topics ranging from pathway discovery to time course analysis. We review exemplary applications of matrix factorization for systems-level analyses. We discuss appropriate application of these methods, their limitations, and focus on analysis of results to facilitate optimal biological interpretation. The inference of biologically relevant features with matrix factorization enables discovery from high-throughput data beyond the limits of current biological knowledge--answering questions from high-dimensional data that we have not yet thought to ask.

systems biology↗

bioRxiv2017

Epigenetic Regulation of Gene Expression in Cancer: Techniques, Resources, and Analysis

Cancer is a complex disease, driven by aberrant activity in numerous signaling pathways in even individual malignant cells. Epigenetic changes are critical mediators of these functional changes that drive and maintain the malignant phenotype. Changes in DNA methylation, histone acetylation and methylation, non-coding RNAs, post-translational modifications are all epigenetic drivers in cancer, independent of changes in the DNA sequence. These epigenetic alterations, once thought to be crucial only for the malignant phenotype maintenance, are now recognized as critical also for disrupting essential pathways that protect the cells from uncontrolled growth, longer survival and establishment in distant sites from the original tissue. In this review, we focus on DNA methylation and chromatin structure in cancer. While associated with cancer, the precise functional role of these alterations is an area of active research using emerging high-throughput approaches and bioinformatics analysis tools. Therefore, this review describes these high-throughput measurement technologies, public domain databases for high-throughput epigenetic data in tumors and model systems, and bioinformatics algorithms for their analysis. Advances in bioinformatics data integration techniques that combine these epigenetic data with genomics data are essential to infer the function of specific epigenetic alterations in cancer, and are therefore also a focus of this review. Future studies using these emerging technologies will elucidate how alterations in the cancer epigenome cooperate with genetic aberrations to cause tumorigenesis initiation and progression. This deeper understanding is essential to future studies that will precisely infer patients prognosis and select patients who will be responsive to emerging epigenetic therapies.

cancer biology↗

bioRxiv2016

PatternMarkers and Genome-Wide CoGAPS Analysis in Parallel Sets (GWCoGAPS) for data-driven detection of novel biomarkers via whole transcriptome Non-negative matrix factorization (NMF)

SummaryNon-negative Matrix Factorization (NMF) algorithms associate gene expression with biological processes (e.g., time-course dynamics or disease subtypes). Compared with univariate associations, the relative weights of NMF solutions can obscure biomarkers. Therefore, we developed a novel PatternMarkers statistic to extract genes for biological validation and enhanced visualization of NMF results. Finding novel and unbiased gene markers with PatternMarkers requires whole-genome data. However, NMF algorithms typically do not converge for the tens of thousands of genes in genome-wide profiling. Therefore, we also developed Genome-Wide CoGAPS Analysis in Parallel Sets (GWCoGAPS), the first robust whole genome Bayesian NMF using the sparse, MCMC algorithm, CoGAPS. This software contains analytic and visualization tools including a Shiny web application, patternMatcher, which are generalized for any NMF. Using these tools, we find granular brain-region and cell-type specific signatures with corresponding biomarkers in GTex data, illustrating GWCoGAPS and patternMarkers ascertainment of data-driven biomarkers from whole-genome data.\n\nAvailabilityPatternMarkers & GWCoGAPS are in the CoGAPS Bioconductor package (3.5) under the GPL license.\n\nContactgsteinobrien@jhmi.edu; ccolantu@jhmi.edu; ejfertig@jhmi.edu

bioinformatics↗