Biology subjects

Brenner, E.

Publications and source records attributed to Brenner, E..

4 recordsLinked to original sources

bioRxiv2020

Integrating multimodal data sets into a mathematical framework to describe and predict therapeutic resistance in cancer

A significant challenge in the field of biomedicine is the development of methods to integrate the multitude of dispersed data sets into comprehensive frameworks to be used to generate optimal clinical decisions. Recent technological advances in single cell analysis allow for high-dimensional molecular characterization of cells and populations, but to date, few mathematical models have attempted to integrate measurements from the single cell scale with other data types. Here, we present a framework that actionizes static outputs from a machine learning model and leverages these as measurements of state variables in a dynamic mechanistic model of treatment response. We apply this framework to breast cancer cells to integrate single cell transcriptomic data with longitudinal population-size data. We demonstrate that the explicit inclusion of the transcriptomic information in the parameter estimation is critical for identification of the model parameters and enables accurate prediction of new treatment regimens. Inclusion of the transcriptomic data improves predictive accuracy in new treatment response dynamics with a concordance correlation coefficient (CCC) of 0.89 compared to a prediction accuracy of CCC = 0.79 without integration of the single cell RNA sequencing (scRNA-seq) data directly into the model calibration. To the best our knowledge, this is the first work that explicitly integrates single cell clonally-resolved transcriptome datasets with longitudinal treatment response data into a mechanistic mathematical model of drug resistance dynamics. We anticipate this approach to be a first step that demonstrates the feasibility of incorporating multimodal data sets into identifiable mathematical models to develop optimized treatment regimens from data.

cancer biology↗

bioRxiv2019

Why some size illusions affect grip aperture

There is an extensive literature debating whether perceived size is used to guide grasping. A possible reason for not using judged size is that using judged positions might lead to more precise movements. As this argument does not hold for small objects, and all studies showing an effect of the Ebbinghaus illusion on grasping used small objects, we hypothesized that size information is used for small objects but not for large ones. Using a modified diagonal illusion, we obtained an effect of about 10% on perceptual judgements, without an effect on grasping, irrespective of object size. We therefore reject our precision hypothesis. We discuss the results in the framework of grasping as moving digits to positions on an object. We conclude that the reported disagreement on the effect of illusions is because the Ebbinghaus illusion not only affects size, but -unlike most size illusions- also affects perceived positions.

neuroscience↗

bioRxiv2019

Single cell transcriptome profiling of the human alcohol-dependent brain

BackgroundAlcoholism remains a prevalent health concern throughout the world. Previous studies have identified transcriptomic patterns in the brain associated with alcohol dependence in both humans and animal models. But none of these studies have systematically investigated expression within the unique cell types present in the brain.\n\nResultsWe utilized single nucleus RNA sequencing (snRNA-seq) to examine the transcriptomes of over 16,000 nuclei isolated from prefrontal cortex of alcoholic and control individuals. Each nucleus was assigned to one of seven major cell types by unsupervised clustering. Cell type enrichment patterns varied greatly among neuroinflammatory-related genes, which are known to play roles in alcohol dependence and neurodegeneration. Differential expression analysis identified cell type-specific genes with altered expression in alcoholics. The largest number of differentially expressed genes (DEGs), including both protein-coding and non-coding, were detected in astrocytes, oligodendrocytes, and microglia.\n\nConclusionsTo our knowledge, this is the first single cell transcriptome analysis of alcohol-associated gene expression in any species, and the first such analysis in humans for any addictive substance. These findings greatly advance understanding of transcriptomic changes in the brain of alcohol-dependent individuals.

neuroscience↗

bioRxiv2019

Expressed barcodes enable clonal characterization of chemotherapeutic responses in chronic lymphocytic leukemia

The remarkable evolutionary capacity of cancer is a major challenge to current therapeutic efforts. Fueling this evolution is its vast clonal heterogeneity and ability to adapt to diverse selective pressures. Although the genetic and transcriptional mechanisms underlying these responses have been independently evaluated, the ability to couple genetic alterations present within individual clones to their respective transcriptional or functional outputs has been lacking in the field. To this end, we developed a high-complexity expressed barcode library that integrates DNA barcoding with single-cell RNA sequencing through use of the CROP-seq sgRNA expression/capture system, and which is compatible with the COLBERT clonal isolation workflow for subsequent genomic and epigenomic characterization of specific clones of interest. We applied this approach to study chronic lymphocytic leukemia (CLL), a mature B cell malignancy notable for its genetic and transcriptomic heterogeneity and variable disease course. Here, we demonstrate the clonal composition and gene expression states of HG3, a CLL cell line harboring the common alteration del(13q), in response to front-line cytotoxic therapy of fludarabine and mafosfamide (an analog of the clinically used cyclophosphamide). Analysis of clonal abundance and clonally-resolved single-cell RNA sequencing revealed that only a small fraction of clones consistently survived therapy. These rare highly drug tolerant clones comprise 94% of the post-treatment population and share a stable, pre-existing gene expression state characterized by upregulation of CXCR4 and WNT signaling and a number of DNA damage and cell survival genes. Taken together, these data demonstrate at unprecedented resolution the diverse clonal characteristics and therapeutic responses of a heterogeneous cancer cell population. Further, this approach provides a template for the high-resolution study of thousands of clones and the respective gene expression states underlying their response to therapy.

cancer biology↗