Biology subjects

Dwarshuis, N. J.

Publications and source records attributed to Dwarshuis, N. J..

3 recordsLinked to original sources

bioRxiv2023

StratoMod: Predicting sequencing and variant calling errors with interpretable machine learning

Despite the variety in sequencing platforms, mappers, and variant callers, no single pipeline is optimal across the entire human genome. Therefore, developers, clinicians, and researchers need to make tradeoffs when designing pipelines for their application. Currently, assessing such tradeoffs relies on intuition about how a certain pipeline will perform in a given genomic context. We present Stratomod, which addresses this problem using an interpretable machine-learning classifier to predict variant calling errors in a data-driven manner. We showed Stratomod can precisely quantify the likelihood of missing variants using Hifi or Illumina, and leveraged Stratomods interpretability to measure contributions from difficult-to-map and homopolymer regions for each respective outcome. Furthermore, we used Statomod to assess the likelihood of missing variants due to mismapping using linear vs. graph-based references, and identified the hard-to-map regions where graph-based methods excelled and by how much. For these we utilized our new benchmark based on the Q100 HG002 assembly, which contains previously-inaccessible difficult regions. Furthermore, Stratomod presents a new method of finding likely false negatives, which is an improvement over current pipelines which only filter false positives. We anticipate this being useful for performing precise risk-reward analyses when designing variant calling pipelines.

genomics↗

bioRxiv2021

Predicting T Cell Quality During Manufacturing Through an Artificial Intelligence-based Integrative Multi-Omics Analytical Platform

Large-scale, reproducible manufacturing of therapeutic cells with consistently high quality is vital for translation to clinically effective and widely accessible cell therapies. However, the biological and logistical complexity of manufacturing a living product, including challenges associated with their inherent variability and uncertainties of process parameters, currently make it difficult to achieve predictable cell-product quality. Using a degradable microscaffold-based T cell process as an example, we developed an Artificial Intelligence (AI)-driven experimental-computational platform to identify a set of critical process parameters (CPP) and critical quality attributes (CQA) from heterogeneous, high dimensional, time-dependent multi-omics data, measurable during early stages of manufacturing and predictive of end-of-manufacturing product quality. Sequential, Design-of-Experiment (DOE)-based studies, coupled with an agnostic machine-learning framework, were used to extract feature combinations from media assessment that were highly predictive of total live CD4+ and CD8+ naive and central memory (CD63L+CCR7+) T cells and their ratio in the end-product. This computational workflow could be broadly applied to any cell therapy and provide a roadmap for discovering CQAs and CPPs in cell manufacturing.

bioengineering↗

bioRxiv2019

Functionalized microcarriers improve T cell manufacturing by facilitating migratory memory T cell production and increasing CD4/CD8 ratio

Adoptive cell therapies (ACT) using chimeric antigen receptor (CAR) T cells have shown promise in treating cancer, but manufacturing large numbers of high quality cells remains challenging. Critically, current T cell expansion technologies only partially recapitulate the in vivo microenvironment found in the human lymph nodes. In these organs, T cells expand at high cell density with autocrine/paracrine signaling, as well as signals from the extracellular matrix (ECM). Here we describe a T cell expansion system using degradable gelatin microcarriers functionalized with anti-CD3 and anti-CD28 monoclonal antibodies (mAbs), which address several of these shortcomings. We show that using this system, we can achieve approximately 2-fold greater expansion compared to functionalized magnetic beads, the current industry standard. Furthermore, carriers generated higher numbers of CCR7+CD62L+ migratory, central memory T cells and CD4+ T cells across multiple donors. Both these phenotypes have emerged as important for establishing durable and effective responses in patients receiving T cell immunotherapies. We further demonstrate that carriers can achieve greater memory cell yield compared to beads across a range of IL2 concentrations from 20 U/mL to 100 U/mL. These differences were greater at lower IL2 concentrations, indicating that the carriers are more efficient. We optimized this system using a design of experiments (DOE) approach and found that the carrier concentration affects the memory cell yield in a quadratic manner, where high or low concentrations are detrimental to memory formation. Finally, we show that carriers do not hinder CAR transduction and can maintain the CD4 and memory phenotype advantages in CAR-transduced T cells.

bioengineering↗