Biology subjects

Connell, W.

Publications and source records attributed to Connell, W..

5 recordsLinked to original sources

bioRxiv2024

PertEval-scFM: Benchmarking Single-Cell Foundation Models for Perturbation Effect Prediction

In silico modeling of transcriptional responses to perturbations is crucial for advancing our understanding of cellular processes and disease mechanisms. We present PertEval-scFM, a standardized framework designed to evaluate models for perturbation effect prediction. We apply PertEval-scFM to benchmark zero-shot single-cell foundation model (scFM) embeddings against base-line models to assess whether these contextualized representations enhance perturbation effect prediction. Our results show that scFM embeddings do not provide consistent improvements over baseline models, especially under distribution shift. Overall, this study provides a systematic evaluation of zero-shot scFM embeddings for perturbation effect prediction, highlighting the challenges of this task and revealing the limitations of current-generation scFMs. Our findings underscore the need for specialized models and high-quality datasets that capture a broader range of cellular states. Source code and documentation can be found at: https://github.com/aaronwtr/PertEval.

bioinformatics↗

bioRxiv2024

DNA-Diffusion: Leveraging Generative Models for Controlling Chromatin Accessibility and Gene Expression via Synthetic Regulatory Elements

The challenge of systematically modifying and optimizing regulatory elements for precise gene expression control is central to modern genomics and synthetic biology. Advancements in generative AI have paved the way for designing synthetic sequences with the aim of safely and accurately modulating gene expression. We leverage diffusion models to design context-specific DNA regulatory sequences, which hold significant potential toward enabling novel therapeutic applications requiring precise modulation of gene expression. Our framework uses a cell type-specific diffusion model to generate synthetic 200 bp regulatory elements based on chromatin accessibility across different cell types. We evaluate the generated sequences based on key metrics to ensure they retain properties of endogenous sequences: transcription factor binding site composition, potential for cell type-specific chromatin accessibility, and capacity for sequences generated by DNA diffusion to activate gene expression in different cell contexts using state-of-the-art prediction models. Our results demonstrate the ability to robustly generate DNA sequences with cell type-specific regulatory potential. DNA-Diffusion paves the way for revolutionizing a regulatory modulation approach to mammalian synthetic biology and precision gene therapy.

synthetic biology↗

bioRxiv2021

Predicting Cellular Drug Sensitivity using Conditional Modulation of Gene Expression

Selecting drugs most effective against a tumors specific transcriptional signature is an important challenge in precision medicine. To assess oncogenic therapy options, cancer cell lines are dosed with drugs that can differentially impact cellular viability. Here we show that basal gene expression patterns can be conditioned by learned small molecule structure to better predict cellular drug sensitivity, achieving an R2 of 0.7190{+/-}0.0098 (a 5.61% gain). We find that 1) transforming gene expression values by learned small molecule representations outperforms raw feature concatenation, 2) small molecule structural features meaningfully contribute to learned representations, and 3) an affine transformation best integrates these representations. We analyze conditioning parameters to determine how small molecule representations modulate gene expression embeddings. This ongoing work formalizes in silico cellular screening as a conditional task in precision oncology applications that can improve drug selection for cancer treatment.

bioinformatics↗

bioRxiv2021

Nurturing Diversity and Inclusion in AI in Biomedicine through a Virtual Summer Program for High School Students

Artificial Intelligence (AI) has the power to improve our lives through a wide variety of applications, many of which fall into the healthcare space; however, a lack of diversity is contributing to flawed systems that perpetuate gender and racial biases, and limit how broadly AI can help people. The UCSF AI4ALL program was established in 2019 to address this issue by promoting diversity and inclusion in AI. The program targets high school students from underrepresented backgrounds in AI and gives them a chance to learn about AI with a focus on biomedicine. In 2020, the UCSF AI4ALL three-week program was held entirely online due to the COVID-19 pandemic. Thus students participated virtually to gain experience with AI, interact with diverse role models in AI, and learn about advancing health through AI. Specifically, they attended lectures in coding and AI, received an in-depth research experience through hands-on projects exploring COVID-19, and engaged in mentoring and personal development sessions with faculty, researchers, industry professionals, and undergraduate and graduate students, many of whom were women and from underrepresented racial and ethnic backgrounds. At the conclusion of the program, the students presented the results of their research projects at our final symposium. Comparison of pre- and post-program survey responses from students demonstrated that after the program, significantly more students were familiar with how to work with data and to evaluate and apply machine learning algorithms. There was also a nominally significant increase in the students knowing people in AI from historically underrepresented groups, feeling confident in discussing AI, and being aware of careers in AI. We found that we were able to engage young students in AI via our online training program and nurture greater inclusion in AI.

scientific communication and education↗

bioRxiv2018

A single cell transcriptomic analysis of human neocortical development

Defining the number, proportion, or lineage of distinct cell types in the developing human brain is an important goal of modern brain research. We defined single cell transcriptomic profiles for 40,000 cells at mid-gestation to identify cell types in the developing human neocortex. We define expression profiles corresponding to all known major cell types at this developmental period and identify multiple transcription factors and co-factors expressed in specific cell types, providing an unprecedented resource for understanding human neocortical development including the first single-cell characterization of human subplate neurons. We characterize major developmental trajectories during early neurogenesis, showing that cell type differentiation occurs on a continuum that involves transitions that tie cell cycle progression with early cell fate decisions. We use these data to deconvolute regulatory networks and map neuropsychiatric disease genes to specific cell types, implicating dysregulation of specific cell types, as the mechanistic underpinnings of several neurodevelopmental disorders. Together these results provide an extensive catalog of cell types in human neocortex and extend our understanding of early cortical development, human brain evolution and the cellular basis of neuropsychiatric disease.\n\nOne Sentence SummaryComprehensive single cell transcriptomes in developing human cortex inform models of cell diversity, differentiation and disease risk.

neuroscience↗