Biology subjects

Raphael, B.

Publications and source records attributed to Raphael, B..

At least 19 recordsLinked to original sources

bioRxiv2026

Engaging the host adaptive response to nutrient limitation delays cancer cachexia

Cancer cachexia is a debilitating wasting syndrome associated with involuntary weight loss, reduced appetite and food intake, adipose and muscle atrophy, and systemic inflammation with induction of a myriad of candidate cachexia factors. Using a multi-omic approach in a genetically engineered mouse model of lung cancer cachexia, we found that early circulating metabolic alterations preceded overt weight loss. Although both cachexia and fasting are characterized by a reduction in food intake, we show that the systemic metabolic changes in cachexia are distinct, indicating a lack of induction in the normal protective response to negative energy balance during cachexia. Cancer cachexia causes profound hepatic reprogramming, and activation of inflammatory programs, alongside muscle atrophy and elevation of circulating cytokines. However, targeting individual candidate cachectic factors such as IL-6, TNF-, GDF15, CCL2, LIF, and IL17A failed to rescue weight loss or prolong survival. In contrast, caloric restriction initiated prior to tumor induction elicited a program of protection during tumor progression that attenuated multi-tissue inflammatory and catabolic transcriptional responses, preserved lean mass, and prolonged survival. Together, these findings suggest that metabolic preconditioning broadly dampens systemic inflammation and delays the onset and progression of cancer cachexia.

cancer biology↗

bioRxiv2026

LAML-Pro: Maximum Likelihood Inference of Cell Genotypes and Cell Lineage Trees

MotivationRecent dynamic lineage tracing technologies use genome editing to induce heritable mutations, or edits, that accumulate across successive cell divisions. These edits are measured using single-cell sequencing or imaging, providing data to reconstruct cell lineages at single-cell resolution. Current computational approaches to infer cell lineage trees, or phylogenies, from these data perform two separate steps: (1) Identify each cells edits (genotype) from the raw sequencing or imaging data; (2) Infer a cell lineage tree from the cell genotypes. However, genotyping cells is an inexact process and genotype errors can yield an inaccurate lineage tree. For example, using fluorescence based-imaging to measure edits results in a high fraction ({approx} 25-50%) of uncertain or erroneous genotypes. ResultsWe introduce Lineage Analysis via Maximum Likelihood with PRobabilistic Observations (LAML-Pro), an algorithm that jointly infers cell genotypes and a cell lineage tree. LAML-Pro is based on the Probabilistic Mixed-type Missing Observation (PMMO) model, which we derive to describe both the genome editing and genotype observation processes. LAML-Pro constructs lineage trees from thousands of cells in under an hour by leveraging the sparsity of transitions under the PMMO model. On simulated data, we demonstrate that LAML-Pro corrects genotype errors and infers substantially more accurate trees than existing methods which are vulnerable to genotype errors. Applied to data from two recent imaging-based lineage tracing systems, LAML-Pro reduces genotype errors by 5-fold and produces more spatially coherent lineage trees compared to existing methods. Availability and ImplementationLAML-Pro is freely available at: github.com/raphael-group/LAML-Pro.

bioinformatics↗

bioRxiv2026

Multimodal spatial alignment and morphology mapping with MOSAICField

Recent efforts to build comprehensive tissue and tumor atlases leverage diverse spatial technologies to measure transcriptomic, proteomic, epigenetic, and other modalities with hundreds to thousands of features at thousands to millions of spatially resolved locations in a tissue slice. Integrating such data across spatial technologies that differ in molecular features, spatial resolution, and tissue morphology remains a major challenge. We introduce MultimOdal Spatial Alignment and Integration with Coordinate neural Field (MOSAICField), a unified framework for aligning spatial slices across arbitrary combinations of experimental modalities. MOSAICField computes two types of spatial alignments across multiple slices from the same tissue: physical alignment, which reconstructs a contiguous 3D model of the original tissue, and morphological alignment, which maps distinct morphological or anatomical structures, such as ducts, veins, or neurons, that may traverse the tissue at different angles relative to the direction of slicing. MOSAICField computes both alignments using a deep neural network that estimates a nonlinear deformation field with a multimodal feature loss. We evaluate MOSAICField on simulated data and a prostate cancer sample from the Human Tumor Atlas Network (HTAN), containing more than a dozen spatial slices with multimodal profiling data. MOSAICField constructs an accurate 3D tumor model, tracks the architecture of the prostatic ductal system, and improves analysis of features within and across modalities, outperforming existing methods.

bioinformatics↗

bioRxiv2026

POTTR: Identifying Recurrent Trajectories in Evolutionary and Developmental Processes using Posets

Multiple biological processes, including cancer evolution and organismal development, are described as a sequence of events with a temporal ordering. While cancer evolves independently in each patient, DNA sequencing studies have shown that in some cancers different patients share specific orders of mutations and these correlate with distinct morphology, drug response, and treatment outcomes. Several methods have been developed to identify such recurrent trajectories of genetic events from phylogenetic trees, but this is complicated by high intra- and inter-tumor heterogeneity as well as uncertainty in the inferred tumor phylogenies including the ambiguous orders between some mutations. We formalize the problem of finding recurrent mutation trajectories using a novel framework of incomplete partially ordered sets (posets), which generalize representations used in previous works and explicitly account for the uncertainty in tumor phylogenies. We define the problem of identifying the largest recurrent trajectories shared in at least k input phylogenies as the maximum k-common induced incomplete subposet (MkCIIS) problem, which we show is NP-hard. We present a combinatorial algorithm, POsets for Temporal Trajectory Resolution (POTTR), to solve the MkCIIS problem using a conflict graph that models recurrent trajectories as independent sets. Thereby we identify maximum recurrent trajectories while resolving multiple sources of uncertainty, like mutation clusters, in the phylogenetic data. We apply POTTR to TRACERx non-small cell lung cancer bulk sequencing and acute myeloid leukemia single-cell sequencing data and through resolution of mutation clusters discover previously unreported trajectories of high statistical significance. On lineage tracing data of an in vitro embryoid model, POTTR identifies conserved differentiation routes across biological replicates and how these routes change in response to chemical perturbations.

bioinformatics↗

bioRxiv2025

Inferring Cell Differentiation Dynamics with Unobserved Progenitors

A cell differentiation map describes the transitions between cell types during a developmental process, and determining this map is a key challenge in developmental biology. Recent single-cell lineage tracing technologies generate lineage trees that describe the history of cell divisions during a developmental process but do not directly measure differentiation events between cell types. Current approaches to infer cell differentiation maps from cell lineage trees make unrealistic assumptions about the developmental process, do not allow for unobserved progenitor cell types, or do not infer cell-type specific rates of growth and transitions. To address these issues, we introduce TROUPE, a likelihood-based framework that infers differentiation and growth dynamics directly from leaf-labeled cell lineage trees while allowing for unobserved progenitor types via biologically motivated potency constraints. We provide an efficient algorithm for computing the maximum likelihood transition and growth rates, as well as a simple model-selection scheme to choose the number of unobserved types. On simulations, TROUPE recovers transition and growth rates better than previous approaches even when there are unobserved types. We apply TROUPE to a model of mammalian development, Trunk-Like Structures (TLS), and show that TROUPE computes reasonable rates of self-renewal and differentiation in both standard and perturbed conditions.

developmental biology↗

bioRxiv2025

TWEAK is increased in ulcerative colitis and contributes to fibroblast-mediated monocyte activation via heterologous non-canonical NF-kB/STAT3 signalling

Background and AimsInteractions between fibroblasts and monocytes have emerged as a contributing factor in IBD pathogenesis and therapy resistance, owing to the ability of both cell types to participate in tissue inflammation and repair. We have previously shown that the TNF superfamily factor TWEAK (TNFSF12) can induce a UC-like inflammatory profile in colonic fibroblasts in vitro, in turn promoting monocyte adhesion and activation. However, the mechanisms underlying fibroblast-monocyte communication and its dysregulation in colitis are incompletely understood. MethodsHere we use co-culture models, human biopsies from ulcerative colitis (UC) patients and healthy donors, and public single-cell transcriptomics to characterise the mechanisms underlying fibroblast-mediated monocyte activation. ResultsWe show that TWEAK-treated inflammatory fibroblasts induce a transcriptional programme that resembles early monocyte/macrophage intermediates in UC and is enriched for genes associated with resistance to anti-TNF (TREM1, OSM, IL1B) and susceptibility to IBD (NOD2, ATG16L1). We find that conditioned media from TWEAK-treated fibroblast causes a sustained activation of STAT3 phosphorylation in monocytes, and that inhibition of the NF-{kappa}B Inducing Kinase (NIK) impairs the ability of inflammatory fibroblasts to activate STAT3 phosphorylation in monocytes, resulting in reduced expression of inflammatory mediators. Using tissues from UC patients, we show that the expansion of CD90+/PDPN+ inflammatory fibroblasts in UC correlates with the accumulation of TWEAK+ myeloid cells in the colonic mucosa, and that these fibroblasts co-localise with infiltrating monocytes in sites of active inflammation. ConclusionTogether, our findings suggest that the TWEAK/NF-{kappa}B/STAT3 axis represents an attractive target to tune inflammatory stroma/monocyte crosstalk.

immunology↗

bioRxiv2025

Deriving longitudinal tumor phylogenies from single-cell sequencing data

Tumors evolve over time and in response to treatment, leading to changes in the proportions of clones within the tumor. Single-cell sequencing of tumor samples from multiple timepoints enables the reconstruction of clonal evolution and tracking of temporal changes in tumor composition. However, the high rates of missing data in single-cell sequencing complicate evolutionary analyses, and can lead to implausible conclusions, such as the reappearance of extinct clones. We introduce PO_SCPLOWHYLLOCHRONC_SCPLOW, an algorithm that builds a longitudinal phylogeny from cancer cells sequenced from multiple timepoints and evaluates whether the constraints on clonal proportions imposed by longitudinal sampling are well supported by the data. PO_SCPLOWHYLLOCHRONC_SCPLOW relies on a novel mathematical formulation of a longitudinal perfect phylogeny, an extension of the perfect phylogeny model that is widely used in cancer evolution. We show that PO_SCPLOWHYLLOCHRONC_SCPLOW outperforms existing phylogeny inference methods on simulated single-cell sequencing data. Applied to longitudinal single-cell DNA sequencing data from an acute myeloid leukemia (AML) patient, PO_SCPLOWHYLLOCHRONC_SCPLOW constructs a longitudinal phylogeny containing rare cancer clones that persist through multiple cycles of targeted drug treatment, a crucial finding missed by existing phylogeny inference methods. The PO_SCPLOWHYLLOCHRONC_SCPLOW statistical test supports the presence of these rare clones.

genomics↗

bioRxiv2025

The tree labeling polytope: a unified approach to ancestral reconstruction problems

MotivationReconstructing unobserved ancestral states of a phylogenetic tree provides insight into the history of evolving systems and is one of the fundamental problems in phylogenetics. For a fixed phylogenetic tree, the most parsimonious ancestral reconstruction - a solution to the small parsimony problem - can be efficiently found using the dynamic programming algorithms of Fitch-Hartigan and Sankoff. Ancestral reconstruction is important in many applications including inferring the routes of metastases in cancer, deriving the transmission history of viruses, determining the direction of cellular differentiation in organismal development, and detecting recombination and horizontal gene transfer in phylogenetic networks. However, most of these applications impose additional global constraints on the reconstructed ancestral states, which break the local structure required in the recurrences of Fitch-Hartigan and Sankoff. ResultsWe introduce an alternative, polyhedral approach to ancestral reconstruction problems using the tree labeling polytope, a geometric object whose vertices represent the feasible ancestral labelings of a tree. This framework yields a polynomial-time linear programming algorithm for the small parsimony problem. More importantly, the tree labeling polytope facilitates the incorporation of additional constraints that arise in modern ancestral reconstruction problems. We demonstrate the utility of our approach by deriving mixed-integer programming algorithms with a small number of integer variables and strong linear relaxations for three such problems: the parsimonious migration history problem, the softwired small parsimony problem on phylogenetic networks, and the convex recoloring problem on trees. Our algorithms outperform existing state-of-the-art methods on both simulated and real datasets. For instance, our algorithm scales to trace routes of cancer metastases in trees with thousands of leaves, enabling the analysis of large trees generated by recent single-cell sequencing technologies. On a mouse model of metastatic lung adenocarcinoma, the tree labeling polytope allows us to infer simpler migration histories compared to previous results. AvailabilityPython implementations of the algorithms provided in this work are available at: github.com/raphael-group/tree-labeling-polytope.

bioinformatics↗

bioRxiv2025

Learning Latent Trajectories in Developmental Time Series with Hidden-Markov Optimal Transport

Deriving the sequence of transitions between cell types, or differentiation events, that occur during organismal development is one of the fundamental challenges in developmental biology. Single-cell and spatial sequencing of samples from different developmental timepoints provide data to investigate differentiation but inferring a sequence of differentiation events requires: (1) finding trajectories, or ancestor:descendant relationships, between cells from consecutive timepoints; (2) coarse-graining these trajectories into a differentiation map, or collection of transitions between cell types, rather than individual cells. We introduce Hidden-Markov Optimal Transport (HM -OT), an algorithm that simultaneously groups cells into cell types and learns transitions between these cell types from developmental transcriptomics time series. HM -OT uses low-rank optimal transport to simultaneously align samples in a time series and learn a sequence of clusterings and a differentiation map with minimal total transport cost. We assume that the law governing cell-type trajectories is characterized by the joint law on consecutive time points, tantamount to a Markov assumption on these latent trajectories. HM -OT can learn these clusterings in a fully unsupervised manner or can generate the least-cost cell type differentiation map consistent with a given set of cell type labels. We validate the unsupervised clusters and cell type differentiation map output by HM -OT on a Stereo-seq dataset of zebrafish development, and we demonstrate the scalability of HM -OT to a massive Stereo-seq dataset of mouse embryonic development. Code availabilitySoftware is available at https://github.com/raphael-group/HM-OT

bioinformatics↗

bioRxiv2025

Joint imputation and deconvolution of gene expression across spatial transcriptomics platforms

Spatially resolved transcriptomics (SRT) technologies measure gene expression across thousands of spatial locations within a tissue slice. Multiple SRT technologies are currently available and others are in active development with each technology having varying spatial resolution (subcellular, single-cell, or multicellular regions), gene coverage (targeted vs. whole-transcriptome), and sequencing depth per location. For example, the widely used 10x Genomics Visium platform measures whole transcriptomes from multiple-cell-sized spots, while the 10x Genomics Xenium platform measures a few hundred genes at subcellular resolution. A number of studies apply multiple SRT technologies to slices that originate from the same biological tissue. Integration of data from different SRT technologies can overcome limitations of the individual technologies enabling the imputation of expression from unmeasured genes in targeted technologies and/or the deconvolution of ad-mixed expression from technologies with lower spatial resolution. We introduce Spatial Integration for Imputation and Deconvolution (SIID), an algorithm to reconstruct a latent spatial gene expression matrix from a pair of observations from different SRT technologies. SIID leverages a spatial alignment and uses a joint non-negative factorization model to accurately impute missing gene expression and infer gene expression signatures of cell types from ad-mixed SRT data. In simulations involving paired SRT datasets from different technologies (e.g., Xenium and Visium), SIID shows superior performance in reconstructing spot-to-cell-type assignments, recovering cell-type-specific gene expression, and imputing missing data compared to contemporary tools. When applied to real-world 10x Xenium-Visium pairs from human breast and colon cancer tissues, SIID achieves highest performance in imputing holdout gene expression. A PyTorch implementation of SIID is available at https://github.com/raphael-group/siid.

bioinformatics↗

bioRxiv2025

Fast tumor phylogeny regression via tree-structured dual dynamic programming

MotivationReconstructing the evolutionary history of tumors from bulk DNA sequencing of multiple tissue samples remains a challenging computational problem, requiring simultaneous deconvolution of the tumor tissue and inference of its evolutionary history. Recently, phylogenetic reconstruction methods have made significant progress by breaking the reconstruction problem into two parts: a regression problem over a fixed topology and a search over tree space. While effective techniques have been developed for the latter search problem, the regression problem remains a bottleneck in both method design and implementation due to the lack of fast, specialized algorithms. ResultsHere, we introduce fastppm, a fast tool to solve the regression problem via tree-structured dual dynamic programming. fastppm supports arbitrary, separable convex loss functions including the{ell} 2, piecewise linear, binomial and beta-binomial loss and provides asymptotic improvements for the{ell} 2 and piecewise linear loss over existing algorithms. We find that fastppm empirically outperforms both specialized and general purpose regression algorithms, obtaining 50-450x speedups while providing as accurate solutions as existing approaches. Incorporating fastppm into several phylogeny inference algorithms immediately yields up to 400x speedups, requiring only a small change to the program code of existing software. Finally, fastppm enables analysis of low-coverage bulk DNA sequencing data on both simulated data and in a patient-derived mouse model of colorectal cancer, outperforming state-of-the-art phylogeny inference algorithms in terms of both accuracy and runtime. Availabilityfastppm is implemented in C++ and available as both a command-line interface and Python library at github.com/elkebir-group/fastppm.git.

bioinformatics↗

bioRxiv2024

Inferring cell differentiation maps from lineage tracing data

During development, mulitpotent cells differentiate through a hierarchy of increasingly restricted progenitor cell types until they realize specialized cell types. A cell differentiation map describes this hierarchy, and inferring these maps is an active area of research spanning traditional single marker lineage studies to data-driven trajectory inference methods on single-cell RNA-seq data. Recent high-throughput lineage tracing technologies profile lineages and cell types at scale, but current methods to infer cell differentiation maps from these data rely on simple models with restrictive assumptions about the developmental process. We introduce a mathematical framework for cell differentiation maps based on the concept of potency, and develop an algorithm, Carta, that infers an optimal cell differentiation map from single-cell lineage tracing data. The key insight in Carta is to balance the trade-off between the complexity of the cell differentiation map and the number of unobserved cell type transitions on the lineage tree. We show that Carta more accurately infers cell differentiation maps on both simulated and real data compared to existing methods. In models of mammalian trunk development and mouse hematopoiesis, Carta identifies important features of development that are not revealed by other methods including convergent differentiation of specialized cell types, progenitor differentiation dynamics, and the refinement of routes of differentiation via new intermediate progenitors. Code availabilityCarta software is available at https://github.com/raphael-group/CARTA

bioinformatics↗

bioRxiv2024

Quantifying higher-order epistasis: beware the chimera

Epistasis, or interactions in which alleles at one locus modify the fitness effects of alleles at other loci, plays a fundamental role in genetics, protein evolution, and many other areas of biology. Epistasis is typically quantified by computing the deviation from the expected fitness under an additive or multiplicative model using one of several formulae. However, these formulae are not all equivalent. Importantly, one widely used formula - which we call the chimeric formula - measures deviations from a multiplicative fitness model on an additive scale, thus mixing two measurement scales. We show that for pairwise interactions, the chimeric formula yields a different magnitude, but the same sign (synergistic vs. antagonistic) of epistasis compared to the multiplicative formula that measures both fitness and deviations on a multiplicative scale. However, for higher-order interactions, we show that the chimeric formula can have both different magnitude and sign compared to the multiplicative formula -- thus confusing negative epistatic interactions with positive interactions, and vice versa. We resolve these inconsistencies by deriving fundamental connections between the different epistasis formulae and the parameters of the multivariate Bernoulli distribution. Our results demonstrate that the additive and multiplicative epistasis formulae are more mathematically sound than the chimeric formula. Moreover, we demonstrate that the mathematical issues with the chimeric epistasis formula lead to markedly different biological interpretations of real data. Analyzing multi-gene knockout data in yeast, multi-way drug interactions in E. coli, and deep mutational scanning (DMS) of several proteins, we find that 10 - 60% of higher-order interactions have a change in sign with the multiplicative or additive epistasis formula. These sign changes result in qualitatively different findings on functional divergence in the yeast genome, synergistic vs. antagonistic drug interactions, and and epistasis between protein mutations. In particular, in the yeast data, the more appropriate multiplicative formula identifies nearly 500 additional negative three-way interactions, thus extending the trigenic interaction network by 25%.

genetics↗

bioRxiv2024

A latent variable model for evaluating mutual exclusivity and co-occurrence between driver mutations in cancer

A key challenge in cancer genomics is understanding the functional relationships and dependencies between combinations of somatic mutations that drive cancer development. Such driver mutations frequently exhibit patterns of mutual exclusivity or co-occurrence across tumors, and many methods have been developed to identify such dependency patterns from bulk DNA sequencing data of a cohort of patients. However, while mutual exclusivity and co-occurrence are described as properties of driver mutations, existing methods do not explicitly disentangle functional, driver mutations from neutral, passenger mutations. In particular, nearly all existing methods evaluate mutual exclusivity or co-occurrence at the gene level, marking a gene as mutated if any mutation - driver or passenger - is present. Since some genes have a large number of passenger mutations, existing methods either restrict their analyses to a small subset of suspected driver genes - limiting their ability to identify novel dependencies - or make spurious inferences of mutual exclusivity and co-occurrence involving genes with many passenger mutations. We introduce DIALECT, an algorithm to identify dependencies between pairs of driver mutations from somatic mutation counts. We derive a latent variable mixture model for drivers and passengers that combines existing probabilistic models of passenger mutation rates with a latent variable describing the unknown status of a mutation as a driver or passenger. We use an expectation maximization (EM) algorithm to estimate the parameters of our model, including the rates of mutually exclusivity and co-occurrence between drivers. We demonstrate that DIALECT more accurately infers mutual exclusivity and co-occurrence between driver mutations compared to existing methods on both simulated mutation data and somatic mutation data from 5 cancer types in The Cancer Genome Atlas (TCGA).

cancer biology↗

bioRxiv2024

Inferring allele-specific copy number aberrations and tumor phylogeography from spatially resolved transcriptomics

A key challenge in cancer research is to reconstruct the somatic evolution within a tumor over time and across space. Spatially resolved transcriptomics (SRT) measures gene expression at thousands of spatial locations in a tumor, but does not directly reveal genetic aberrations. We introduce CalicoST, an algorithm to simultaneously infer allele-specific copy number aberrations (CNAs) and a spatial model of tumor evolution from SRT of tumor slices. By modeling CNA-induced perturbations in both total and allele-specific gene expression, CalicoST identifies important types of CNAs - including copy-neutral loss of heterozygosity (CNLOH) and mirrored subclonal CNAs- that are invisible to total copy number analysis. On SRT data from nine patients from the Human Tumor Atlas Network (HTAN) with matched whole exome sequencing (WES) data, CalicoST achieves an average accuracy of 86%, approximately 21% higher than existing methods. On two patients with SRT data from multiple adjacent slices, CalicoST reconstructs a tumor phylogeography that describes the spread of cancerous clones in three-dimensional space. CalicoST analysis of multiple SRT slices from a cancerous prostate organ reveals five spatially coherent clones, with mirrored subclonal CNAs distinguishing clones on the two sides of the prostate, forming a bifurcating phylogeography in both genetic and physical space.

bioinformatics↗

bioRxiv2024

DeST-OT: Alignment of Spatiotemporal Transcriptomics Data

Spatially resolved transcriptomics (SRT) measures mRNA transcripts at thousands of locations within a tissue slice, revealing spatial variations in gene expression and distribution of cell types. In recent studies, SRT has been applied to tissue slices from multiple timepoints during the development of an organism. Alignment of this spatiotemporal transcriptomics data can provide insights into the gene expression programs governing the growth and differentiation of cells over space and time. We introduce DeST-OT (Developmental SpatioTemporal Optimal Transport), a method to align SRT slices from pairs of developmental timepoints using the framework of optimal transport (OT). DeST-OT uses semi-relaxed optimal transport to precisely model cellular growth, death, and differentiation processes that are not well-modeled by existing alignment methods. We demonstrate the advantage of DeST-OT on simulated slices. We further introduce two metrics to quantify the plausibility of a spatiotemporal alignment: a growth distortion metric which quantifies the discrepancy between the inferred and the true cell type growth rates, and a migration metric which quantifies the distance traveled between ancestor and descendant cells. DeST-OT outperforms existing methods on these metrics in the alignment of spatiotemporal transcriptomics data from the development of axolotl brain. Code availabilitySoftware is available at https://github.com/raphael-group/DeST_OT

bioinformatics↗

bioRxiv2024

Maximum Likelihood Inference of Time-scaled Cell Lineage Trees with Mixed-type Missing Data

Recent dynamic lineage tracing technologies combine CRISPR-based genome editing with single-cell sequencing to track cell divisions during development. A key computational problem in dynamic lineage tracing is to infer a cell lineage tree from the measured CRISPR-induced mutations. Three features of dynamic lineage tracing data distinguish this problem from standard phylogenetic tree inference. First, the CRISPR-editing process modifies a genomic location exactly once. This non-modifiable property is not well described by the time-reversible models commonly used in phylogenetics. Second, as a consequence of non-modifiability, the number of mutations per time unit decreases over time. Third, CRISPR-based genome-editing and single-cell sequencing results in high rates of both heritable and non-heritable (dropout) missing data. To model these features, we introduce the Probabilistic Mixed-type Missing (PMM) model. We describe an algorithm, LAML (Lineage Analysis via Maximum Likelihood), to search for the maximum likelihood (ML) tree under the PMM model. LAML combines an Expectation Maximization (EM) algorithm with a heuristic tree search to jointly estimate tree topology, branch lengths and missing data parameters. We derive a closed-form solution for the M-step in the case of no heritable missing data, and a block coordinate ascent approach in the general case which is more efficient than the standard General Time Reversible (GTR) phylogenetic model. On simulated data, LAML infers more accurate tree topologies and branch lengths than existing methods, with greater advantages on datasets with higher ratios of heritable to non-heritable missing data. We show that LAML provides unbiased time-scaled estimates of branch lengths. In contrast, we demonstrate that maximum parsimony methods for lineage tracing data not only underestimate branch lengths, but also yield branch lengths which are not proportional to time, due to the nonlinear decay in the number of mutations on branches further from the root. On lineage tracing data from a mouse model of lung adenocarcinoma, we show that LAML infers phylogenetic distances that are more concordant with gene expression data compared to distances derived from maximum parsimony. The LAML tree topology is more plausible than existing published trees, with fewer total cell migrations between distant metastases and fewer reseeding events where cells migrate back to the primary tumor. Crucially, we identify three distinct time epochs of metastasis progression, which includes a burst of metastasis events to various anatomical sites during a single month. Softwarehttps://github.com/raphael-group/LAML Data availabilityhttps://github.com/raphael-group/laml-experiments

systems biology↗

bioRxiv2023

PASTE2: Partial Alignment of Multi-slice Spatially Resolved Transcriptomics Data

Spatially resolved transcriptomics (SRT) technologies measure mRNA expression at thousands of locations in a tissue slice. However, nearly all SRT technologies measure expression in two dimensional slices extracted from a three-dimensional tissue, thus losing information that is shared across multiple slices from the same tissue. Integrating SRT data across multiple slices can help recover this information and improve downstream expression analyses, but multi-slice alignment and integration remains a challenging task. Existing methods for integrating SRT data either do not use spatial information or assume that the morphology of the tissue is largely preserved across slices, an assumption that is often violated due to biological or technical reasons. We introduce PASTE2, a method for partial alignment and 3D reconstruction of multi-slice SRT datasets, allowing only partial overlap between aligned slices and/or slice-specific cell types. PASTE2 formulates a novel partial Fused Gromov-Wasserstein Optimal Transport problem, which we solve using a conditional gradient algorithm. PASTE2 includes a model selection procedure to estimate the fraction of overlap between slices, and optionally uses information from histological images that accompany some SRT experiments. We show on both simulated and real data that PASTE2 obtains more accurate alignments than existing methods. We further use PASTE2 to reconstruct a 3D map of gene expression in a Drosophila embryo from a 16 slice Stereo-seq dataset. PASTE2 produces accurate alignments of multi-slice datasets from multiple SRT technologies, enabling detailed studies of spatial gene expression across a wide range of biological applications. Code availabilitySoftware is available at https://github.com/raphael-group/paste2

bioinformatics↗