Biology subjects

Frost, H. R.

Publications and source records attributed to Frost, H. R..

At least 19 recordsLinked to original sources

bioRxiv2026

Biological foundation models illuminate annotation blind spots in evolutionarily divergent genomes

Chromosome-scale assemblies are increasingly available for non-model organisms, but functional annotation remains limited when deep evolutionary divergence erodes primary amino-acid sequence identity even though protein structural similarity can remain conserved. We present a hybrid annotation framework that decouples gene-model discovery from cross-species similarity assignment by combining Evo2-based ab initio prediction of exon-intron structures with ESM-2 protein-embedding-based structural similarity mapping. Applied to the sea lamprey, the framework derives high- or medium-confidence cross-species similarity assignments for 73,485 Evo2-derived translated protein models, including 35,395 high-confidence calls, and expands the deduplicated structural catalog to 31,286 loci, including 20,871 additions absent from the Ensembl baseline. A joint alignment-structure classification identifies 21,391 structurally supported catalog loci that a fixed human DIAMOND protein search does not confidently assign on its own, including 21,184 loci with no detectable human protein-sequence match and 207 loci with only low-confidence matches in the classical 20-30% amino-acid-identity twilight zone. These rescue-space totals describe catalog loci rather than validated one-to-one human-absent genes. In a single-cell RNA sequencing application, a stricter UTR-aware Ensembl+Evo2 reference improves gene recovery and expands the interpretable feature space of the lamprey immune compartment relative to the Ensembl baseline. This enables more resolved annotation of four transcriptionally defined immune cell states, including VLRA+-associated T-like and VLRB+-associated B-like programs together with oxidative iron-handling and iron-associated VLR-linked states. Together, these results show that structural protein signal often persists beyond the limits of pairwise sequence alignment and that an embedding-based annotation layer can extend that signal to improve downstream comparative and single-cell analyses in evolutionarily divergent genomes.

bioinformatics↗

bioRxiv2026

Analysis of biological networks using Krylov subspace trajectories

We describe an approach for analyzing biological networks using rows of the Krylov subspace of the adjacency matrix. Specifically, we explore the scenario where the Krylov subspace matrix is computed via power iteration using a non-random and potentially non-uniform initial vector that captures a specific biological state or perturbation. In this case, the rows the Krylov subspace matrix (i.e., Krylov trajectories) carry important functional information about the network nodes in the biological context represented by the initial vector. We demonstrate the utility of this approach for community detection and perturbation analysis using the C. Elegans neural network.

bioinformatics↗

bioRxiv2026

Parsimonious cell co-localization scoring for spatial transcriptomics

Spatial transcriptomics (ST) preserves tissue architecture while profiling gene expression, motivating methods that quantify whether annotated labels (such as cell types) preferentially co-occur in local neighborhoods. We introduce the Neighborhood Product Co-localization (NPC) score, a simple per-cell metric computed on a pruned spatial neighbor graph: for a set of m [≥] 2 labels, NPC is the product of their neighborhood proportions, optionally normalized by expected co-occurrence under independence and paired with permutation-based significance testing. NPC is interpretable (maximized under balanced neighborhoods), efficient to compute, and extends naturally from pairwise to multivariate microenvironment definitions. Using a mouse ovary MERFISH dataset, we show that NPC complements established Squidpy co-occurrence and neighborhood enrichment analyses by localizing co-localization hotspots in tissue space, recapitulating prominent global associations, and highlighting spatially restricted niches such as follicle boundaries; we further demonstrate multivariate NPC scoring by identifying coordinated endothelial-stroma-theca co-localization. Overall, NPC provides a practical framework for interpretable, single-cell resolution co-localization analysis in ST cohorts.

bioinformatics↗

bioRxiv2025

Autoencoder Denoising for Network-Based Spatial Transcriptomics Data with Applications for Cell Signaling Estimation

We propose an autoencoder-based framework for denoising networks estimated from Spatial Transcriptomics (ST) data for cell signaling analysis. Our method consists of an unsupervised encoder-decoder framework for denoising the network adjacency matrix and a supervised framework for cell signaling estimation. We validate our denoising component using the Frobenius norm metric for graphs simulated using the Barabasi-Albert (BA) and Erd[o]s-Renyi (ER) models against reconstructions generated using the singular value decomposition (SVD). We then validate the cell signaling estimates generated using the supervised component on real ST data for the Wnt3-Fzd1 and Ephb1-Efnb3 interactions. We report that our framework achieves better adjacency matrix reconstructions for superlinear BA and dense ER graphs and generates cell signaling estimates that are both regionally specific and biologically plausible. An important contribution of this work is the application of neural networks for network-based cell signaling estimation using ST data and the benchmarking of autoencoder versus SVD denoising for different graph models.

systems biology↗

bioRxiv2025

Benchmarking sketching methods on spatial transcriptomics data

High-throughput spatial transcriptomics (ST) now profiles hundreds of thousands of cells or locations per section, creating computational bottlenecks for routine analysis. Sketching, or intelligent sub-sampling, addresses scale by selecting small, representative subsets. While effective for scRNA-seq data, existing sketching methods, which optimize coverage in expression space but ignore physical location, can introduce spatial bias when applied to ST data. To explore the impact of sketching on ST analysis, we systematically benchmarked uniform sampling, leverage-score sampling, Geosketch (minimax/Hausdorff), and scSampler (maximin) across multiple real ST datasets (mouse ovary, MERFISH brain, human breast cancer, lung) and simulations, using three input representations: PCA embeddings, spatial coordinates, and spatially smoothed embeddings. We show that expression-only designs capture global transcriptomic heterogeneity but distort tissue architecture by over-sampling high-variability regions and under-sampling homogeneous areas. Coordinate-only sampling restores tissue coverage but misses transcriptional extremes. A simple spatially aware extension, computing leverage scores from a randomized SVD basis smoothed by a spatial weights matrix, strikes a favorable balance, recovering rare cell states while maintaining uniform tissue coverage and avoiding edge effects. Across robust Hausdorff distances, clustering stability (ARI), PCA loading drift, and local cell-type MSE, spatially smoothed leverage scores match or outperform alternatives. These results motivate joint spatial-transcriptomic sketching objectives to enable fast, unbiased analyses of increasingly large ST datasets.

bioinformatics↗

bioRxiv2025

Gene set optimization for cancer transcriptomics using sparse principal component analysis

A common approach for exploring pathway dysregulation in cancer involves the gene set or pathway analysis of tumor transcriptomic data. Unfortunately, the effectiveness of cancer gene set testing is limited by the fact that most gene set collections model gene activity in normal tissue, which can differ significantly from gene activity found within tumors. To address this challenge, we have developed a bioinformatics approach based on sparse principal component analysis (PCA) for optimizing existing gene set collections to reflect the pattern of gene activity in dysplastic tissue and have used this technique to optimize the Molecular Signatures Database (MSigDB) Hallmark collection for 21 solid human cancers profiled via bulk RNA-seq by The Tumor Genome Atlas (TCGA). Demonstrating the biological utility of our approach, the average survival association of gene set members is improved after optimization for nearly all cancer types and Hallmark gene sets.

bioinformatics↗

bioRxiv2025

Antibody responses against bacterial glycans affinity mature and diversify in germinal centers.

Anti-carbohydrate antibodies (Abs) play crucial roles in pathogen control, but their generation remains poorly understood. By studying responses to Streptococcus pyogenes in humans, we reveal that the glycan-targeted response shifts from IgM towards IgG and IgA memory with age and antigen exposure across blood, spleen, and tonsils. Both natural colonization and controlled human infection with S. pyogenes increased class-switched B cells, with evidence of within-clone switching. Glycan-specific B cells readily participated in germinal center (GC) responses and showed robust somatic hypermutation despite a molecular signature consistent with receiving reduced T cell help. We conclude that mucosal pathogen encounters elicit glycan responses that class-switch, evolve and diversify through the GC. These findings reveal how age and infection history can influence the quality, quantity, and isotype use of glycan-specific B cells, with implications for the design and schedule of glycan-containing vaccines.

immunology↗

bioRxiv2024

Randomized Spatial PCA (RASP): a computationally efficient method for dimensionality reduction of high-resolution spatial transcriptomics data

Spatial transcriptomics (ST) provides critical insights into the spatial organization of gene expression, enabling researchers to unravel the intricate relationship between cellular environments and biological function. Identifying spatial domains within tissues is key to understanding tissue architecture and mechanisms underlying development and disease progression. Here, we present Randomized Spatial PCA (RASP), a novel spatially-aware dimensionality reduction method for ST data. RASP is designed to be orders-of-magnitude faster than existing techniques, scale to datasets with 100, 000+ locations, support flexible integration of non-transcriptomic covariates, and reconstruct de-noised, spatially-smoothed gene expression values. It employs a randomized two-stage PCA framework and configurable spatial smoothing. RASP was compared to BASS, GraphST, SEDR, SpatialPCA, STAGATE, and CellCharter using diverse ST datasets (10x Visium, Stereo-Seq, MERFISH, 10x Xenium) on human and mouse tissues. In these benchmarks, RASP delivers comparable or superior accuracy in tissue-domain detection while achieving substantial improvements in computational speed. Its efficiency not only reduces runtime and resource requirements but also makes it practical to explore a broad range of spatial-smoothing parameters in a high-throughput fashion. By enabling rapid re-analysis under different parameter settings, RASP empowers users to fine-tune the balance between resolution and noise suppression on large, high-resolution subcellular datasets--a critical capability when investigating complex tissue architecture.

bioinformatics↗

bioRxiv2024

Leveraging cell type-specificity for gene set analysis of single cell transcriptomics

Although single cell RNA-sequencing (scRNA-seq) provides unprecedented insights into the biology of complex tissues, analyzing such data on a gene-by-gene basis is challenging due to the large number of tested hypotheses and consequent low statistical power and difficult interpretation. These issues are magnified by the increased noise, significant sparsity and multi-modal distributions characteristic of single cell data. One promising approach for addressing these challenges is gene set testing, or pathway analysis. Unfortunately, statistical and biological differences between single cell and bulk transcriptomic data make it challenging to use existing gene set collections, which were developed for bulk tissue analysis, on scRNA-seq data. In this paper, we describe a procedure for customizing gene set collections originally created for bulk tissue analysis to reflect the structure of gene activity within specific cell types. Our approach leverages information about mean gene expression in the 81 human cell types profiled via scRNA-seq by the Human Protein Atlas (HPA) Single Cell Type Atlas. This HPA information is used to compute cell type-specific gene and gene set weights that can be used to filter or weight gene set collections. As demonstrated through the analysis of immune cell scRNA-seq data using gene sets from the Molecular Signatures Database (MSigDB), accounting for cell type-specificity can significantly improve gene set testing power and interpretability. An example vignette along with gene and gene set weights for the 81 HPA SCTA cell types and the MSigDB collections are available at https://hrfrost.host.dartmouth.edu/SCGeneSetOpt/.

bioinformatics↗

bioRxiv2024

Streptococcus pyogenes pharyngitis elicits diverse antibody responses to key vaccine antigens influenced by the imprint of past infections.

Knowledge gaps regarding human immunity to Streptococcus pyogenes have impeded vaccine development. To address these gaps and evaluate vaccine candidates, we established a human challenge model of S. pyogenes pharyngitis. Here, we analysed antibody responses in serum and saliva against 19 antigens to identify characteristics distinguishing 19 participants who developed pharyngitis and 6 who did not. Pharyngitis elicited serum IgG responses to key vaccine antigens and a muted mucosal IgA response, whereas the 6 participants without pharyngitis had more pronounced IgA responses and minimal IgG responses. Serum IgG responses to pharyngitis in adult participants resembled those observed in children and were inversely correlated with the magnitude of pre-existing responses. While a straightforward correlate of protection was not evident, baseline antibody signatures distinguished clinical and immunological outcomes following experimental challenge. This highlights the influence of a complex humoral imprint from previous exposure, relevant for interpreting immunogenicity in forthcoming vaccine trials.

immunology↗

bioRxiv2024

A common set of structurally distinct endogenous retroviruses predict survival in multiple human tumor types

Accumulating evidence suggests that viral mimicry induction in cancer cells through the expression of human endogenous retroviral elements (ERVs) enhances tumor immunogenicity and improves response to immunotherapy. However, the characterization of ERV expression across multiple tumor types, their clinical relevance, and their role in tumor immunity remain limited. Here, we assessed the prognostic and immune-related consequences of ERV expression across 8 different tumor types from The Cancer Genome Atlas (TCGA). We applied a recently developed tool, Telescope, to quantify the expression of 14,968 ERVs in bulk tumor RNA-sequencing data. Approximately 15-22% of Telescopes ERVs (navg = 2,736) were expressed in each tumor cohort. We identified both tissue-specific and commonly expressed ERVs across cohorts. Over 50% of tissue-specific ERVs were ubiquitously expressed (n = 1,709) across all 8 tumor types. Using patient clinical data, we identified a subset of 94 ERVs whose expression levels were significantly associated with overall survival, of which 42 were favorably prognostic. Prognostic ERVs displayed unique structural characteristics, including more frequent inverted repeats and more stable secondary structures. Patients were classified based on ERV expression, allowing identification of patient cohorts with greater than 6-fold differences in overall survival. We also found that expression of positively prognostic ERVs was associated with enrichment for selected anti-tumor immune response gene expression signatures. This analysis extends previous results identifying patterns of ERV expression in bulk tumor RNA-seq datasets, providing further insight into the role of ERVs in tumor clinicopathology and immunogenicity.

cancer biology↗

bioRxiv2023

Single cell transcriptomics-level Cytokine Activity Prediction and Estimation (SCAPE)

Cytokine interaction activity modeling is a pressing problem since uncontrolled cytokine influx is at fault in a variety of medical conditions, including viral infections like COVID19, and cancer. Accurate knowledge of cytokine activity levels can be leveraged to provide tailored treatment recommendations based on individual patients transcriptomics data. Here, we describe a novel method named Single cell transcriptomics-level Cytokine Activity Prediction and Estimation (SCAPE) that can predict cell-level cytokine activity from scRNA-seq data. SCAPE generates activity estimates using cytokine-specific gene sets constructed using information from the CytoSig and Reactome databases and scored with a modified version of the Variance-adjusted Mahalanobis (VAM) method adjusted for negative weights. We validate SCAPE using both simulated and real single cell RNA-sequencing (scRNA-seq) data. For the simulation study, we perturb real scRNA-seq data to reflect the expected stimulation signature of up to 41 cytokines, including chemokines, interleukins and growth factors. For the real data evaluation, we use publicly accessible scRNA-seq data that captures cytokine stimulation and blockade experiment conditions and a COVID19 transcriptomics data. As demonstrated by these evaluations, our approach can accurately estimate cell-level cytokine activity from scRNA-seq data. Our model has the potential to be incorporated in clinical settings as a way to estimate cytokine signaling for different cell populations within an impacted tissue sample.

bioinformatics↗

bioRxiv2023

Reconstruction Set Test (RESET): a computationally efficient method for single sample gene set testing based on randomized reduced rank reconstruction error

We have developed a new, and analytically novel, single sample gene set testing method called Reconstruction Set Test (RESET). RESET quantifies gene set importance at both the sample-level and for the entire dataset based on the ability of set genes to reconstruct values for all measured genes. RESET addresses four important limitations of current techniques: 1) existing single sample methods are designed to detect mean differences and struggle to identify differential correlation patterns, 2) computationally efficient techniques are self-contained methods and cannot directly detect competitive scenarios where set genes differ from non-set genes in the same sample, 3) the scores generated by current methods can only be accurately compared across samples for a single set and not between sets, and 4) the computational performance of even the fastest existing methods be significant on very large datasets. RESET is realized using a computationally efficient randomized reduced rank reconstruction algorithm (available via the RESET R package on CRAN) that can effectively detect patterns of differential abundance and differential correlation for self-contained and competitive scenarios. As demonstrated using real and simulated scRNA-seq data, RESET provides superior accuracy at a lower computational cost relative to other single sample approaches.

bioinformatics↗

bioRxiv2023

Cell type-specific Interaction Analysis using Doublets in scRNA-seq (CIcADA)

MotivationDoublets are usually considered an unwanted artifact of single-cell RNA-sequencing (scRNA-seq) and are only identified in datasets for the sake of removal. However, if cells have a juxtacrine attachment to one another in situ and maintain this association through an scRNA-seq processing pipeline that only partially dissociates the tissue, these doublets can provide meaningful biological information regarding the interactions and cell processes occurring in the analyzed tissue. This is especially true for cases such as the immune compartment of the tumor microenvironment, where the frequency and type of immune cell juxtacrine interactions can be a prognostic indicator. ResultsWe developed Cell type-specific Interaction Analysis using Doublets in scRNA-seq (CIcADA) as a pipeline for identifying and analyzing biological doublets in scRNA-seq data. CIcADA identifies putative doublets using multi-label cell type scores and characterizes interaction dynamics through a comparison against synthetic doublets of the same cell type composition. In performing CIcADA on several scRNA-seq tumor datasets, we found that the identified doublets were consistently upregulating expression of immune response genes. ContactCourtney.T.Schiebout.GR@Dartmouth.edu, Hildreth.R.Frost@Dartmouth.edu

bioinformatics↗

bioRxiv2022

STREAK: A Supervised Cell Surface Receptor Abundance Estimation Strategy for Single Cell RNA-Sequencing Data using Feature Selection and Thresholded Gene Set Scoring

The accurate estimation of cell surface receptor abundance for single cell transcriptomics data is important for the tasks of cell type and phenotype categorization and cell-cell interaction quantification. We previously developed an unsupervised receptor abundance estimation technique named SPECK (Surface Protein abundance Estimation using CKmeans-based clustered thresholding) to address the challenges associated with accurate abundance estimation. In that paper, we concluded that SPECK results in improved concordance with Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) data relative to comparative unsupervised abundance estimation techniques using only single-cell RNA-sequencing (scRNA-seq) data. In this paper, we outline a new supervised receptor abundance estimation method called STREAK (gene Set Testing-based Receptor abundance Estimation using Adjusted distances and cKmeans thresholding) that leverages associations learned from joint scRNA-seq/CITE-seq training data and a thresholded gene set scoring mechanism to estimate receptor abundance for scRNA-seq target data. We evaluate STREAK relative to both unsupervised and supervised receptor abundance estimation techniques using two evaluation approaches on three joint scRNA-seq/CITE-seq datasets that represent two human tissue types. We conclude that STREAK outperforms other abundance estimation strategies and provides a more biologically interpretable and transparent statistical model.

bioinformatics↗

bioRxiv2022

SPECK: An Unsupervised Learning Approach for Cell Surface Receptor Abundance Estimation for Single Cell RNA-Sequencing Data

The rapid development of single cell transcriptomics has revolutionized the study of complex tissues. Single cell RNA-sequencing (scRNA-seq) can profile tens-of-thousands of dissociated cells from a tissue sample, enabling researchers to identify cell types, phenotypes and interactions that control tissue structure and function. A key requirement of these applications is the accurate estimation of cell surface protein abundance. Although technologies to directly quantify surface proteins are available, this data is uncommon and limited to proteins with available antibodies. While supervised methods that are trained on Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) data can provide the best performance, this training data is also limited by available antibodies and may not exist for the tissue under investigation. In the absence of protein measurements, researchers must estimate receptor abundance from scRNA-seq data. We thereby developed a new unsupervised method for receptor abundance estimation using scRNA-seq data called SPECK (Surface Protein abundance Estimation using CKmeans-based clustered thresholding) and evaluated its performance against other unsupervised approaches on up to 215 human receptors and multiple tissue types. This analysis reveals that techniques based on a thresholded reduced rank reconstruction (RRR) of scRNA-seq data are effective for receptor abundance estimation with SPECK providing the best overall performance.

bioinformatics↗

bioRxiv2022

Characterizing control of memory CD8 T cell differentiation by BTB-ZF transcription factor Zbtb20

Members of the BTB-ZF transcription factor family regulate the immune system. Our lab identified that family member Zbtb20 contributes to the differentiation, recall responses and metabolism of CD8 T cells. Here, we report a characterization of the transcriptional and epigenetic signatures controlled by Zbtb20 at single-cell resolution during the effector and memory phases of the CD8 T cell response. Without Zbtb20, transcriptional programs associated with memory CD8 T cell formation were upregulated throughout the CD8 T response. A signature of open chromatin was associated with genes controlling T cell activation, consistent with the known impact on differentiation. Additionally, memory CD8 T cells lacking Zbtb20 were characterized by open chromatin regions with overrepresentation of AP-1 transcription factor motifs and elevated RNA- and protein-level expression of the corresponding AP-1 components. Finally, we describe motifs and genomic annotations from the DNA targets of Zbtb20 in CD8 T cells identified by CUT&RUN. Together, these data establish the transcriptional and epigenetic networks contributing to the control of CD8 T cell responses by Zbtb20.

immunology↗

bioRxiv2022

A quaternion model for single cell transcriptomics

We present an approach for modeling single cell RNA-sequencing (scRNA-seq) and spatial transcriptomics (ST) data using quaternions. Quaternions are four dimensional hypercomplex numbers that, along with real numbers, complex numbers and octonions, represent one of the four normed division algebras. Quaternions have been primarily employed to represent three-dimensional rotations in computer graphics with most biomedical applications focused on problems involving the structure and orientation of biomolecules, e.g., protein folding, chromatin conformation, etc. In this paper, we detail an approach for mapping the cells/locations in a scRNA-seq/ST data set to quaternions. According to this model, the quaternion associated with each cell/location represents a vector in [R]3 with vector length capturing sequencing depth and vector direction capturing the relative expression profile. Assuming that biologically interesting features of an scRNA-seq/ST data set are preserved within a rank three reconstruction of the unnormalized counts, this representation has several benefits for data analysis. First, it supports a novel approach for scRNA-seq/ST data visualization that captures cell state uncertainty. Second, the model implies that transformations between cell states can be viewed as three-dimensional rotations, which have a corresponding representation as rotation quaternions. The fact that these rotation quaternions can be interpreted as cells enables a novel approach for characterizing cell state transitions with specific relevance to the analysis of pseudo-temporal ordering trajectories. Most importantly, a quaternion representation supports the genome-wide spectral analysis of scRNA-seq/ST data relative to a single variable, e.g., pseudo-time, or two variables, e.g., spatial coordinates, using a one or two-dimensional hypercomplex discrete Fourier transform. An R package supporting this model and the hypercomplex Fourier analysis of ST data along with several example vignettes is available at https://hrfrost.host.dartmouth.edu/QSC.

bioinformatics↗