Biology subjects

Altschuler, S.

Publications and source records attributed to Altschuler, S..

6 recordsLinked to original sources

bioRxiv2023

Selection of optimal cell lines for high-content phenotypic screening

High-content microscopy offers a scalable approach to screen against multiple targets in a single pass. Prior work has focused on methods to select "optimal" cellular readouts in microscopy screens. However, methods to select optimal cell line models have garnered much less attention. Here, we provide a roadmap for how to select the cell line or lines that are best suited to identify bioactive compounds and their mechanism of action (MOA). We test our approach on compounds targeting cancer-relevant pathways, ranking cell lines in two tasks: detecting compound activity ("phenoactivity") and grouping compounds with similar MOA by similar phenotype ("phenosimilarity"). Evaluating six cell lines across 3214 well-annotated compounds, we show that optimal cell line selection depends on both the task of interest (e.g. detecting phenoactivity vs. inferring phenosimilarity) and distribution of MOAs within the compound library. Given a task of interest and set of compounds, we provide a systematic framework for choosing optimal cell line(s). Our framework can be used to reduce the number of cell lines required to identify hits within a compound library and help accelerate the pace of early drug discovery.

pharmacology and toxicology↗

bioRxiv2022

A scalable screening platform for phenotypic subtyping of ALS patient-derived fibroblasts

A major challenge for understanding and treating Amyotrophic Lateral Sclerosis (ALS) is that most patients have no known genetic cause. Even within defined genetic subtypes, patients display considerable clinical heterogeneity. It is unclear how to identify subsets of ALS patients that share common molecular dysregulation or could respond similarly to treatment. Here, we developed a scalable microscopy and machine learning platform to phenotypically subtype readily available, primary patient-derived fibroblasts. Application of our platform identified robust signatures for the genetic subtype FUS-ALS, allowing cell lines to be scored along a spectrum from FUS-ALS to non-ALS. Our FUS-ALS phenotypic score negatively correlates with age of diagnosis and provides information that is distinct from transcript profiling. Interestingly, the FUS-ALS phenotypic score can be used to identify sporadic patient fibroblasts that have consistent pathway dysregulation with FUS-ALS. Further, we showcase how the score can be used to evaluate the effects of ASO treatment on patient fibroblasts. Our platform provides an approach to move from genetic to phenotypic subtyping and a first step towards rational selection of patient subpopulations for targeted therapies.

systems biology↗

bioRxiv2020

Characterizing tissue composition through combined analysis of single-cell morphologies and transcriptional states

Advances in spatial transcriptomics technologies enable optical profiling of morphological and transcriptional modalities from the same cells within tissues. Here, we present multi-modal structured embedding (MUSE), an approach to deeply characterize tissue heterogeneity through analysis of combined image and transcriptional single-cell measurements. We demonstrate that MUSE can discover cellular subpopulations missed by either modality as well as compensate for modality-specific noise. MUSE identified biologically meaningful cellular subpopulations and stereotyped spatial patterning within heterogeneous mouse cortex brain tissues, profiled by seqFISH+ or STARmap technologies. MUSE provides a framework for combining multi-modal single-cell data to reveal deeper insights into the states, functions and organization of cells in complex biological tissues.

bioinformatics↗

bioRxiv2020

Dissecting the impact of metabolic environment on three common cancer cell phenotypes

The impact of different metabolic environments on cancer cell behavior is poorly understood. Here, we systematically altered nutrient composition of cell culture media and examined the impact on three phenotypes—drug-treatment survival, cell migration, and lactate overflow—that are frequently studied in cancer cells. These perturbations across diverse metabolic environments revealed simple relationships between cell growth rate and drug-treatment survival or migration. In contrast, lactate overflow was highly sensitive to changes in sugar availability but largely insensitive to changes in amino acid availability, regardless of the growth rate. Further investigation suggested that the degree of lactate overflow across metabolic environments is largely determined by the cells’ ability to maintain high rates of sugar uptake. This study enabled us to elucidate quantitative relationships between metabolic environment and cancer cell phenotypes, which echo empirical growth laws discovered to govern analogous phenotypes in microbes.Competing Interest StatementThe authors have declared no competing interest.View Full Text

cancer biology↗

bioRxiv2020

Loss of Tet2 affects proliferation and drug sensitivity through altered dynamics of cell-state transitions

A persistent puzzle in cancer biology is how mutations, which neither alter canonical growth signaling pathways nor directly interfere with drug mechanism, can still recur and persist in tumors. One notable example is the loss-of-function mutation of the DNA demethylase Tet2 in acute myeloid leukemias (AMLs) that frequently persists from diagnosis through remission and relapse (Rothenberg-Thurley et al., 2018; Corces-Zimmerman et al., 2014; Nibourel et al., 2010), but whose fitness advantage in the setting of anti-leukemic chemotherapy is unclear. Here we use paired isogenic human AML cell lines to show that Tet2 loss-of-function alters the dynamics of transitions between differentiated and stem-like states. Mathematical modeling and experimental validation reveal that these altered cell-state dynamics can benefit the cell population by slowing population decay during drug treatment and lowering the number of survivor cells needed to re-establish the initial population. These studies shed light on the functional and phenotypic effects of a Tet2 loss-of-function in AML, illustrate how a single gene mutation can alter a cells phenotypic plasticity, and open up new avenues in the development of strategies to combat AML relapse.

systems biology↗

bioRxiv2018

Massive single-cell RNA-seq analysis and imputation via deep learning

Recent advances in large-scale single cell RNA-seq enable fine-grained characterization of phenotypically distinct cellular states within heterogeneous tissues. We present scScope, a scalable deep-learning based approach that can accurately and rapidly identify cell-type composition from millions of noisy single-cell gene-expression profiles.

bioinformatics↗