Biology subjects

Stanley, N.

Publications and source records attributed to Stanley, N..

16 recordsLinked to original sources

bioRxiv2026

MICRON learns outcome-associated representations of spatial immune microenvironments

Spatial imaging proteomics modalities, such as imaging mass cytometry, enable comprehensive identification of immune microenvironments driving disease outcomes. Identifying outcome-associated immune microenvironments from these data has proven to be complex, as it requires segmenting cells with complex shapes and reconciling spatial signatures across many heterogeneous samples. We present MICRON, a segmentation-free, fully automated multiple-instance learning based tool for automatic identification of outcome-linked immune microenvironments. MICRON learns representations of samples profiled with spatial imaging proteomics modalities, enabling more accurate prognostic and diagnostic prediction over existing approaches. As a case study, we show that MICRON generates a comprehensive importance map that reveals key outcome-associated immune microenvironments in brain cancer, uncovering coordinated cell-cell communication between astrocytes, NK cells, and macrophages linked to survival outcomes. MICRON is provided as open source software for broad use by clinicians and biologists at https://github.com/ChenCookie/micron.

bioinformatics↗

bioRxiv2026

Optimal transport fate mapping resolves T cell differentiation dynamics across tissues

Immune responses evolve across time and tissues through coordinated programs of proliferation, differentiation, and migration, yet most single-cell measurements capture only static molecular snapshots. As a result, reconstructing how immune cells transition between alternative fates remains challenging, particularly for CD8 T cells, whose differentiation is highly dynamic and shaped by rapid expansion, contraction, and tissue trafficking. Here, we introduce an optimal transport-based fate mapping framework that reconstructs continuous CD8 T cell trajectories across time and tissues. Applied to longitudinal single-cell RNA-seq data from CD8 T cells responding to acute viral infection in mice, this approach accurately recapitulates population dynamics and resolves coherent effector and memory T cell differentiation trajectories. Extending the model to multiple tissues, we identify and experimentally validate temporally distinct waves of migration into the small intestine that give rise to divergent tissue-resident memory (TO_SCPLOWRMC_SCPLOW) fates, long-lived T cells crucial in immunosurveillance. By integrating optimal transport inference with time-resolved in vivo labeling, we demonstrate that CD52 marks recent tissue entrants and distinguishes them from TO_SCPLOWRMC_SCPLOW precursors. Finally, trajectory-guided analysis of transcription factor regulons reveals both shared and context-specific gene regulatory programs and identifies AP4 as a key regulator of circulating versus tissue-resident specification. These results establish optimal transport as a principled framework for reconstructing immune cell fate dynamics and provide a quantitative map of early events governing antiviral CD8 T cell differentiation across tissues.

bioinformatics↗

bioRxiv2025

Peripheral immune patterns enable robust cross-platform prediction of ALS onset and progression

Amyotrophic lateral sclerosis (ALS) progression rates vary dramatically between patients, yet the basis of this heterogeneity remains elusive, with no prognostic biomarkers existing to guide clinical decisions or stratify patients for therapeutic trials. Here, we identify a network of coordinated immune cell types, which exhibit differential disruption across progression groups. Using mass cytometry (CyTOF) to profile 2.2 million immune cells from 35 ALS patients stratified by progression rate and 9 healthy controls, we find that the extent of immune dysfunction cannot be reflected by examining differences in individual cell type frequencies. In contrast, analyses of correlation patterns between cell types revealed distinct immune organization patterns, where coordination complexity varied with disease progression. Across all progression groups, we observed striking immune reorganization in natural killer (NK) cells and a major shift from B cell/basophil coordination hubs in healthy controls to neutrophil/T cell-dominated patterns in ALS. Having established coordinated immune patterns, we developed machine learning models to further improve our ability to stratify between disease and non-disease cohorts, achieving superior performance compared to models using cell frequencies alone. Central and effector memory (CM/EM) CD4+ T cell interactions emerged as top discriminative features for disease status, while plasmacytoid dendritic cell (pDC) relationships, especially their ratio with regulatory T cells (T-regs), distinguished progression rates, supporting T-reg-based therapeutic approaches. These findings reframe ALS as a disease of immune coordination breakdown, pointing towards cell-type specific therapeutics and biomarkers that may extend beyond ALS to other neurodegenerative diseases characterized by immune dysfunction.

immunology↗

bioRxiv2025

Single-cell and cell-type-level insights into stress-driven immune regulation relevant to disease

Environmental stress contributes to several disease conditions - including cardiovascular disease, mental illness, and autoimmune disorders - through immune dysregulation. Stress operates through the hypothalamic-pituitary-adrenal and sympathoadrenal axes, leading to peripheral secretion of cortisol and norepinephrine, respectively. While stress profoundly influences immune function, the underlying single-cell and cell-type-specific mechanisms remain poorly understood. To address this knowledge gap, we employed an ex vivo model investigating the transcriptomic responses of primary peripheral blood mononuclear cells and neutrophils to physiological stress levels (i.e., levels reached during in vivo stress) of cortisol and norepinephrine at single-cell resolution. We identified novel stress-hormone-dependent and cell-type-specific transcriptomic changes, including uniquely regulated genes and inflammation-related signatures within distinct innate (e.g., CD163+ monocytes/macrophages, neutrophils) and other immune cell types. The single-cell-derived signatures, applied to two independent human cohorts, effectively distinguished stress-related diseases. Our findings provide cell-type-level insights into how stress hormones modulate immune function relevant to stress-related conditions.

immunology↗

bioRxiv2024

Leveraging Single-Cell RNA-Seq to Generate Robust Microglia Aging Clocks

Biological aging clocks - composite molecular markers thought to capture an individuals biological age - have been traditionally developed through bulk-level analyses of mixed cells and tissues. However, recent evidence highlights the importance of gaining single-cell-level insights into the aging process. Microglia are key immune cells in the brain shown to adapt functionally in aging and disease. Recent studies have generated single-cell RNA sequencing (scRNA-seq) datasets that transcriptionally profile microglia during aging and development. Leveraging such datasets, we develop and compare computational approaches for generating transcriptome-wide summaries to establish robust microglia aging clocks. Our results reveal that unsupervised, frequency-based featurization approaches strike a balance in accuracy, interpretability, and computational efficiency. We further extrapolate and demonstrate applicability of such microglia clocks to readily available bulk RNA-seq data with environmental inputs. Single-cell-derived clocks can yield insights into the determinants of brain aging, ultimately promoting interventions that beneficially modulate health and disease trajectories.

neuroscience↗

bioRxiv2024

TDP-43 pathology links innate and adaptive immunity in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis is the most common fatal motor neuron disease. Approximately 90% of ALS patients exhibit pathology of the master RNA regulator, Transactive Response DNA Binding protein (TDP-43). Despite the prevalence TDP-43 pathology in ALS motor neurons, recent findings suggest immune dysfunction is a determinant of disease progression in patients. Whether TDP-43 pathology elicits disease-modifying immune responses in ALS remains underexplored. In this study, we demonstrate that TDP-43 pathology is internalized by antigen presenting cells, causes vesicle rupture, and leads to innate and adaptive immune cell activation. Using a multiplex imaging platform, we observed interactions between innate and adaptive immune cells near TDP-43 pathological lesions in ALS brain. We used a mass cytometry-based whole-blood stimulation assay to provide evidence that ALS patient peripheral immune cells exhibit responses to TDP-43 aggregates. Taken together, this study provides a novel link between TDP-43 pathology and ALS immune dysfunction, and further highlights the translational and diagnostic implications of monitoring and manipulating the ALS immune response.

cell biology↗

bioRxiv2023

scLKME: A Landmark-based Approach for Generating Multi-cellular Sample Embeddings from Single-cell Data

Single-cell technologies enable high-dimensional profiling of individual cells, therefore offering profound insights into subtle variation between specialized cell-types. However, translating the multitude of nuanced cellular profiles into meaningful per-sample representations is challenging due to heterogeneous cellular composition across individual profiled samples. To compute informative per-sample representations, we developed scLKME, a novel approach that uses a landmark-based kernel mean embedding method to convert multi-sample single-cell data into compact per-sample embeddings. Treating each sample as a distribution over cells, scLKME identifies landmarks across samples and maps these distributions into a reproducing kernel Hilbert space. Overall, scLKME outperforms state-of-the-art techniques in robustness, efficiency, accuracy, and practical usefulness of sample embeddings. Its application on a CyTOF dataset profiling immune responses in preterm birth highlighted its capacity to accurately identify patient-specific variations correlating with gestational age, suggesting broad applicability to multi-sample single-cell datasets with complex experimental designs. scLKME is available as an open-sourced python package at https://github.com/CompCy-lab/scLKME.

bioinformatics↗

bioRxiv2023

The Interplay Between Sketching and Graph Generation Algorithms in Identifying Biologically Cohesive Cell-Populations in Single-Cell Data

High-throughput single-cell immune profiling technologies, such as mass cytometry (CyTOF) and single-cell RNA sequencing measure the expression of multiple proteins or genes across many individual cells within a profiled sample. As it is often of interest to identify particular clusters or cell-populations driving clinical phenotypes or experimental outcomes, there is a critical need to develop automated bioinformatics approaches that can handle a large number of profiled cells. For analyzing multi-sample single-cell datasets at scale, the datasets are usually encoded as a graph, where nodes represent cells and edges imply significant between-cell similarity. As multi-sample single-cell experiments can readily result in millions of profiled cells, the construction and analysis of a graph becomes computationally prohibitive and often requires reducing the dataset size through downsampling as a pre-processing step. Here, we explore the interplay between sketching, or downsampling approaches, and the way in which the graph is constructed on the sketched data for ultimately identifying biologically-meaningful cell-populations. Our results suggest that combining a principled sketching approach with a simple k-nearest neighbor graph representation of the data can identify meaningful subsets of cells as robustly as, and sometimes better than, more sophisticated graph generation approaches. This reveals that the practical concern of downsampling or sketching a limited number of cells is a more critical pre-processing step than how the graph representation is constructed.

bioinformatics↗

bioRxiv2023

Feature selection for preserving biological trajectories in single-cell data

Single-cell technologies can readily measure the expression of thousands of molecular features from individual cells undergoing dynamic biological processes, such as cellular differentiation, immune response, and disease progression. While examining cells along a computationally ordered pseudotime offers the potential to study how subtle changes in gene or protein expression impact cell fate decision-making, identifying characteristic features that drive continuous biological processes remains difficult to detect from unenriched and noisy single-cell data. Given that all profiled sources of feature variation contribute to the cell-to-cell distances that define an inferred cellular trajectory, including confounding sources of biological variation (e.g. cell cycle or metabolic state) or noisy and irrelevant features (e.g. measurements with low signal-to-noise ratio) can mask the underlying trajectory of study and hinder inference. Here, we present DELVE (dynamic selection of locally covarying features), an unsupervised feature selection method for identifying a representative subset of dynamically-expressed molecular features that recapitulates cellular trajectories. In contrast to previous work, DELVE uses a bottom-up approach to mitigate the effect of unwanted sources of variation confounding inference, and instead models cell states from dynamic feature modules that constitute core regulatory complexes. Using simulations, single-cell RNA sequencing data, and iterative immunofluorescence imaging data in the context of the cell cycle and cellular differentiation, we demonstrate that DELVE selects features that more accurately characterize cell populations and improve the recovery of cell type transitions. This feature selection framework provides an alternative approach for improving trajectory inference and uncovering co-variation amongst features along a biological trajectory. DELVE is implemented as an open-source python package and is publicly available at: https://github.com/jranek/delve.

bioinformatics↗

bioRxiv2023

Cellograph: A Semi-supervised Approach to Analyzing Multi-condition Single-cell RNA-sequencing Data Using Graph Neural Networks

AO_SCPLOWBSTRACTC_SCPLOWWith the growing number of single-cell datasets collected under more complex experimental conditions, there is an opportunity to leverage single-cell variability to reveal deeper insights into how cells respond to perturbations. Many existing approaches rely on discretizing the data into clusters for differential gene expression (DGE), effectively ironing out any information unveiled by the single-cell variability across cell-types. In addition, DGE often assumes a statistical distribution that, if erroneous, can lead to false positive differentially expressed genes. Here, we present Cellograph: a semi-supervised framework that uses graph neural networks to quantify the effects of perturbations at single-cell granularity. Cellograph not only measures how prototypical cells are of each condition but also learns a latent space that is amenable to interpretable data visualization and clustering. The learned gene weight matrix from training reveals pertinent genes driving the differences between conditions. We demonstrate the utility of our approach on publicly-available datasets including cancer drug therapy, stem cell reprogramming, and organoid differentiation. Cellograph outperforms existing methods for quantifying the effects of experimental perturbations and offers a novel framework to analyze single-cell data using deep learning.

bioinformatics↗

bioRxiv2023

Benchmarking differential abundance methods for finding condition-specific prototypical cells in multi-sample single-cell datasets

Modern single-cell data analysis relies on statistical testing (e.g. differential expression testing) to identify genes or proteins that are up-or down-regulated in relation to cell-types or clinical outcomes. However, existing algorithms for such statistical testing are often limited by technical noise and cellular heterogeneity, which lead to false-positive results. To constrain the analysis to a compact and phenotype-related cell population, differential abundance (DA) testing methods were employed to identify subgroups of cells whose abundance changed significantly in response to disease progression, or experimental perturbation. Despite the effectiveness of DA testing algorithms of identifying critical cell-states, there are no systematic benchmarking or comparative studies to compare their usages in practice. Herein, we performed the first comprehensive benchmarking study to objectively evaluate and compare the benefits and potential downsides of current state-of-the-art DA testing methods. We benchmarked six DA testing methods on several practical tasks, using both synthetic and real single-cell datasets. The task evaluated include, recognizing true DA subpopulations, appropriate handing of batch effects, runtime efficiency, and hyperparameter usability and robustness. Based on various evaluation results, this paper gives dataset-specific suggestions for the usage of DA testing methods.

bioinformatics↗

bioRxiv2022

A Graph Coarsening Algorithm for Compressing Representations of Single-Cell Data with Clinical or Experimental Attributes

Graph-based algorithms have become essential in the analysis of single-cell data for numerous tasks, such as automated cell-phenotyping and identifying cellular correlates of experimental perturbations or disease states. In large multi-patient, multi-sample single-cell datasets, the analysis of cell-cell similarity graphs representations of these data becomes computationally prohibitive. Here, we introduce cytocoarsening, a novel graph-coarsening algorithm that significantly reduces the size of single-cell graph representations, which can then used as input to downstream bioinformatics algorithms for improved computational efficiency. Uniquely, cytocoarsening considers both phenotypical similarity of cells and similarity of cells associated clinical or experimental attributes in order to more readily identify condition-specific cell populations. The resulting coarse graph representations were evaluated based on both their structural correctness and the capacity of downstream algorithms to uncover the same biological conclusions as if the full graph had been used. Cytocoarsening is provided as open source code at https://github.com/ChenCookie/cytocoarsening.

bioinformatics↗

bioRxiv2022

Integrating temporal single-cell gene expression modalities for trajectory inference and disease prediction

Current methods for analyzing single-cell datasets have relied primarily on static gene expression measurements to characterize the molecular state of individual cells. However, capturing temporal changes in cell state is crucial for the interpretation of dynamic phenotypes such as the cell cycle, development, or disease progression. RNA velocity infers the direction and speed of transcriptional changes in individual cells, yet it is unclear how these temporal gene expression modalities may be leveraged for predictive modeling of cellular dynamics. Here, we present the first task-oriented benchmarking study that investigates integration of temporal sequencing modalities for dynamic cell state prediction. We benchmark eight integration approaches on eight datasets spanning different biological contexts, sequencing technologies, and species. We find that integrated data more accurately infers biological trajectories and achieves increased performance on classifying cells according to perturbation and disease states. Furthermore, we show that simple concatenation of spliced and unspliced molecules performs consistently well on classification tasks and can be used over more memory intensive and computationally expensive methods. This work provides users with practical recommendations for task-specific integration of single-cell gene expression modalities.

bioinformatics↗

bioRxiv2021

CytoSet: Predicting clinical outcomes via set-modeling of cytometry data

AO_SCPLOWBSTRACTC_SCPLOWSingle-cell flow and mass cytometry technologies are being increasingly applied in clinical settings, as they enable the simultaneous measurement of multiple proteins across millions of cells within a multi-patient cohort. In this work, we introduce CytoSet, a deep learning model that can directly predict a patients clinical outcome from a collection of cells obtained through a blood or tissue sample. Unlike previous work, CytoSet explicitly models the cells profiled in each patient sample as a set, allowing for the use of recently developed permutation invariant architectures. We show that CytoSet achieves state-of-the-art classification performance across a variety of flow and mass cytometry benchmark datasets. The strong classification performance is further complemented by demonstrated robustness to the number of sub-sampled cells per patient and the depth of model, enabling CytoSet to scale adequately to hundreds of patient samples. The strong performance achieved by the set-based architectures used in CytoSet suggests that clinical cytometry data can be appropriately interpreted and studied as sets. The code is publicly available at https://github.com/CompCy-lab/cytoset.

bioinformatics↗

bioRxiv2020

FastPG: Fast clustering of millions of single cells

Current single-cell experiments can produce datasets with millions of cells. Unsupervised clustering can be used to identify cell populations in single-cell analysis but often leads to interminable computation time at this scale. This problem has previously been mitigated by subsampling cells, which greatly reduces accuracy. We built on the graph-based algorithm PhenoGraph and developed FastPG which has the same cell assignment accuracy but is on average 27x faster in our tests. FastPG also has higher cell assignment accuracy than two other fast clustering methods, FlowSOM and PARC. AvailabilityFastPG is available here: https://github.com/sararselitsky/FastPG

bioinformatics↗

bioRxiv2020

Integration of Mechanistic Immunological Knowledge into a Machine Learning Pipeline Increases Predictive Power

The dense network of interconnected cellular signaling responses quantifiable in peripheral immune cells provide a wealth of actionable immunological insights. While high-throughput single-cell profiling techniques, including polychromatic flow and mass cytometry, have matured to a point that enables detailed immune profiling of patients in numerous clinical settings, limited cohort size together with the high dimensionality of data increases the possibility of false positive discoveries and model overfitting. We introduce a machine learning platform, the immunological Elastic-Net (iEN), which incorporates immunological knowledge directly into the predictive models. Importantly, the algorithm maintains the exploratory nature of the high-dimensional dataset, allowing for the inclusion of immune features with strong predictive power even if not consistent with prior knowledge. In three independent studies our method demonstrates improved predictive power for clinically-relevant outcomes from mass cytometry data generated from whole blood, as well as a large simulated dataset.

bioinformatics↗