Biology subjects

Raphael, B.

Publications and source records attributed to Raphael, B..

4 recordsLinked to original sources

bioRxiv2021

Alignment and Integration of Spatial Transcriptomics Data

Spatial transcriptomics (ST) is a new technology that measures mRNA expression across thousands of spots on a tissue slice, while preserving information about the spatial location of spots. ST is typically applied to several replicates from adjacent slices of a tissue. However, existing methods to analyze ST data do not take full advantage of the similarity in both gene expression and spatial organization across these replicates. We introduce a new method PASTE (Probabilistic Alignment of ST Experiments) to align and integrate ST data across adjacent tissue slices leveraging both transcriptional similarity and spatial distances between spots. First, we formalize and solve the problem of pairwise alignment of ST data from adjacent tissue slices, or layers, using Fused Gromov-Wasserstein Optimal Transport (FGW-OT), which accounts for variability in the composition and spatial location of the spots on each layer. From these pairwise alignments, we construct a 3D representation of the tissue. Next, we introduce the problem of simultaneous alignment and integration of multiple ST layers into a single layer with a low rank gene expression matrix. We derive an algorithm to solve the problem by alternating between solving FGW-OT instances and solving a Non-negative Matrix Factorization (NMF) of a weighted expression matrix. We show on both simulated and real ST datasets that PASTE accurately aligns spots across adjacent layers and accurately estimates a consensus expression matrix from multiple ST layers. PASTE outperforms integration methods that rely solely on either transcriptional similarity or spatial similarity, demonstrating the advantages of combining both types of information. Code availabilitySoftware is available at https://github.com/raphael-group/paste

bioinformatics↗

bioRxiv2021

DeCiFering the elusive cancer cell fraction in tumor heterogeneity and evolution

Most tumors are heterogeneous mixtures of normal cells and cancer cells, with individual cancer cells distinguished by somatic mutations that accumulated during the evolution of the tumor. The fundamental quantity used to measure tumor heterogeneity from somatic single-nucleotide variants (SNVs) is the Cancer Cell Fraction (CCF), or proportion of cancer cells that contain the SNV. However, in tumors containing copy-number aberrations (CNAs) - e.g. most solid tumors - the estimation of CCFs from DNA sequencing data is challenging because a CNA may alter the mutation multiplicity, or number of copies of an SNV. Existing methods to estimate CCFs rely on the restrictive Constant Mutation Multiplicity (CMM) assumption that the mutation multiplicity is constant across all tumor cells containing the mutation. However, the CMM assumption is commonly violated in tumors containing CNAs, and thus CCFs computed under the CMM assumption may yield unrealistic conclusions about tumor heterogeneity and evolution. The CCF also has a second limitation for phylogenetic analysis: the CCF measures the presence of a mutation at the present time, but SNVs may be lost during the evolution of a tumor due to deletions of chromosomal segments. Thus, SNVs that co-occur on the same phylogenetic branch may have different CCFs. In this work, we address these limitations of the CCF in two ways. First, we show how to compute the CCF of an SNV under a less restrictive and more realistic assumption called the Single Split Copy Number (SSCN) assumption. Second, we introduce a novel statistic, the descendant cell fraction (DCF), that quantifies both the prevalence of an SNV and the past evolutionary history of SNVs under an evolutionary model that allows for mutation losses. That is, SNVs that co-occur on the same phylogenetic branch will have the same DCF. We implement these ideas in an algorithm named DeCiFer. DeCiFer computes the DCFs of SNVs from read counts and copy-number proportions and also infers clusters of mutations that are suitable for phylogenetic analysis. We show that DeCiFer clusters SNVs more accurately than existing methods on simulated data containing mutation losses. We apply DeCiFer to sequencing data from 49 metastatic prostate cancer samples and show that DeCiFer produces more parsimonious and reasonable reconstructions of tumor evolution compared to previous approaches. Thus, DeCiFer enables more accurate quantification of intra-tumor heterogeneity and improves downstream inference of tumor evolution. Code availabilitySoftware is available at https://github.com/raphael-group/decifer

bioinformatics↗

bioRxiv2021

Spatially Interacting Phosphorylation Sites and Mutations in Cancer

Advances in mass-spectrometry have generated increasingly large-scale proteomics datasets containing tens of thousands of phosphorylation sites (phosphosites) that require prioritization. We develop a bioinformatics tool called HotPho and systematically discover 3D co-clustering of phosphosites and cancer mutations on protein structures. HotPho identifies 474 such hybrid clusters containing 1,255 co-clustering phosphosites, including RET p.S904/Y928, the conserved HRAS/KRAS p.Y96, and IDH1 p.Y139/IDH2 p.Y179 that are adjacent to recurrent mutations on protein structures not found by linear proximity approaches. Hybrid clusters, enriched in histone and kinase domains, frequently include expression-associated mutations experimentally shown as activating and conferring genetic dependency. Approximately 300 co-clustering phosphosites are verified in patient samples of 5 cancer types or previously implicated in cancer, including CTNNB1 p.S29/Y30, EGFR p.S720, MAPK1 p.S142, and PTPN12 p.S275. In summary, systematic 3D clustering analysis highlights nearly 3,000 likely functional mutations and over 1,000 cancer phosphosites for downstream investigation and evaluation of potential clinical relevance.

bioinformatics↗

bioRxiv2020

Cystoliths in Ficus leaves: increasing carbon fixation in saturating light by light scattering off a mineral substrate

The manner in which leaves adapt to different light intensities is key for enabling plants to survive in diverse environments and in constantly changing conditions. Many studies have addressed this subject, but little attention has been given to the effect that mineral deposits in leaves can have on photosynthesis. Here we study 6 species of Ficus and investigate how different cystolith configurations affect photosynthesis in both non-saturating and saturating light. We quantified the effect of light scattering by cystoliths on light absorption by measuring chlorophyll fluorescence intensity using microfluorimetry. We complement this by carbon assimilation measurements to directly estimate how light scattering by cystoliths affects the overall photosynthetic process. We show that light waste is reduced when irradiance is on a cystolith compared to cystolith free tissue. Moreover, light is channeled into the center of the leaf where photosynthesis occurs more efficiently than in the outer layers. This, in turn, leads to more efficient CO2 assimilation. We conclude that cystoliths contribute to photosynthesis optimization under saturating light. Cystoliths reduce the wasted portion of absorbed light under saturating irradiance by scattering light into the light-deprived leaf center. The increased efficiency may well provide important benefits to plants that form mineral scatterers.

plant biology↗