Biology subjects

Staklinski, S. J.

Publications and source records attributed to Staklinski, S. J..

5 recordsLinked to original sources

bioRxiv2026

Lineage-informed factor analysis reveals heritable programs of single-cell gene expression

Single-cell transcriptomics has transformed our ability to characterize cellular identity, but present-day gene expression profiles capture only a snapshot of a process that unfolds across cell division history. Recent single-cell lineage-tracing technologies make it possible to reconstruct cell division histories for thousands of cells, opening a window into how gene expression evolves. Yet observed gene expression is often redundant, with correlations among genes reflecting underlying latent biological programs and regulatory networks. To capture this structure, we introduce scPFA, a single-cell phylogenetic factor analysis framework that represents gene expression through a small set of latent factors that evolve along lineages under a phylogenetic prior, capturing correlated structure hidden in present-day observations alone. Simulations demonstrate accurate recovery of latent factors and covariance structure across conditions. Applied to developmental and cancer lineage-tracing datasets, the model uncovers biologically interpretable, lineage-associated expression programs. Together, these results demonstrate how lineage-informed factor analysis can reveal temporal biological structure hidden within high-dimensional single-cell data.

bioinformatics↗

bioRxiv2026

Lineage-aware stochastic modeling reveals gene-expression dynamics in development and disease

Gene expression changes along cell lineages, but most single-cell RNA-seq analyses treat cells as independent snapshots and ignore their phylogenetic relationships. Here we present LaVOUS, a lineage-aware probabilistic framework for modeling sparse single-cell gene-expression counts on reconstructed lineage trees. LaVOUS couples Brownian motion and Ornstein-Uhlenbeck models of latent transcriptional dynamics with negative-binomial observation models and scalable variational inference, enabling likelihood-based tests for gene-expression heritability, branch-specific expression shifts, and ancestral expression reconstruction. In simulations, LaVOUS improved detection of lineage-associated expression changes over Gaussian phylogenetic models and accurately reconstructed expression histories across expression levels. Applied to lineage-resolved single-cell datasets from metastatic lung cancer, class-switching B cells, and the developing brain, LaVOUS identified expression changes associated with metastatic progression, isotype switching, and neuronal differentiation. LaVOUS provides a general framework for studying single-cell expression dynamics across development and disease.

bioinformatics↗

bioRxiv2025

Variational Inference with Node Embeddings (VINE) for Scalable Bayesian Phylogenetics

Bayesian methods are now widely used in reconstructing both species and cell-lineage phylogenies, but they remain heavily reliant on computationally intensive Markov chain Monte Carlo sampling. Phylogenetic variational inference (VI) circumvents this dependency but so far has been limited in speed and scalability. Here we introduce Variational Inference with Node Embeddings (VO_SCPLOWINEC_SCPLOW), a computational method that combines an embedding of taxa in a high-dimensional space and a distance-based "decoder" with several algorithmic innovations to dramatically improve phylogenetic VI. VO_SCPLOWINEC_SCPLOW supports both standard DNA substitution models and CRISPR barcode-mutation models for inference of cell-lineage trees and tissue-migration histories. In extensive simulation experiments, we show that VO_SCPLOWINEC_SCPLOW can effectively approximate the results of the best available Bayesian methods with speeds orders of magnitude faster. We then apply VO_SCPLOWINEC_SCPLOW to [~]1,000 complete SARS-CoV-2 genomes and [~]900 lung-cancer cell barcodes, showing reductions in compute time from days to hours or minutes.

bioinformatics↗

bioRxiv2025

Bayesian inference of tissue-migration histories in metastatic cancer from cell-lineage tracing data

Cell-lineage tracing now enables direct study of tissue migration in metastatic cancer, but current reconstruction algorithms are limited by a reliance on strong parsimony assumptions and pre-estimated cell-lineage phylogenies. Here, we introduce a probabilistic modeling and inference framework, called BEAM (Bayesian Evolutionary Analysis of Metastasis), that provides richer information about complex metastatic histories. Based on the flexible BEAST 2 platform for Bayesian phylogenetics, BEAM infers a full posterior distribution over cell-lineage phylogenies and tissue migration graphs, complete with timing information. We show using simulated data that BEAM reliably outperforms current methods for inference of tissue migration graphs, especially for more complex histories. We then apply BEAM to public data sets for lung and prostate cancer, finding support for distinct modes of migration across clones and reseeding of primary tumors. Overall, BEAM serves as a powerful framework for revealing the modes, timing, and directionality of tissue migration in metastatic cancer.

cancer biology↗

bioRxiv2023

Utility of AlphaMissense predictions in Asparagine Synthetase deficiency variant classification

AlphaMissense is a recently developed method that is designed to classify missense variants into pathogenic, benign, or ambiguous categories across the entire human proteome. Asparagine Synthetase Deficiency (ASNSD) is a developmental disorder associated with severe symptoms, including congenital microcephaly, seizures, and premature death. Diagnosing ASNSD relies on identifying mutations in the asparagine synthetase (ASNS) gene through DNA sequencing and determining whether these variants are pathogenic or benign. Pathogenic ASNS variants are predicted to disrupt the proteins structure and/or function, leading to asparagine depletion within cells and inhibition of cell growth. AlphaMissense offers a promising solution for the rapid classification of ASNS variants established by DNA sequencing and provides a community resource of pathogenicity scores and classifications for newly diagnosed ASNSD patients. Here, we assessed AlphaMissenses utility in ASNSD by benchmarking it against known critical residues in ASNS and evaluating its performance against a list of previously reported ASNSD-associated variants. We also present a pipeline to calculate AlphaMissense scores for any protein in the UniProt database. AlphaMissense accurately attributed a high average pathogenicity score to known critical residues within the two ASNS active sites and the connecting intramolecular tunnel. The program successfully categorized 78.9% of known ASNSD-associated missense variants as pathogenic. The remaining variants were primarily labeled as ambiguous, with a smaller proportion classified as benign. This study underscores the potential role of AlphaMissense in classifying ASNS variants in suspected cases of ASNSD, potentially providing clarity to patients and their families grappling with ongoing diagnostic uncertainty.

genetics↗