# Deep-learning based profiling of patient-derived cells as a tool for genomic and translational medicine

> **NIH NIH K99** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2021 · $97,780

## Abstract

Project Summary/Abstract:
The genetic landscape of rare and common diseases has emerged as heterogeneous and complex. Already,
researchers and clinicians face the challenge to discern pathophysiological mechanism and treatment
opportunities for hundreds of genetic subtypes that have been identified in rare diseases, such as inherited
neuropathies (INs) or mitochondrial diseases (MiDs) alone. Still, a large fraction of disease loci remains to be
discovered – a daunting task, since gene-identification studies often require immense sample-sizes, which are
difficult to achieve, even for more common conditions. Simultaneously, much of the heritability of many
disorders appears to be determined by the collective impact of possibly thousands of low-impact variants,
spread across the genome. Ideally, the impact of a given set of candidate variants could be assessed within
high-throughput framework that accounts for the genetic context of individual patients. Leveraging advanced
deep learning algorithms, we have developed an unbiased, scalable method to rapidly identify disease-
associated phenotypes in high-resolution, multiplexed, fluorescent microscopy images of primary, patient
derived cells. In turn, the discovered phenotypes can be exploited as experimental signals against which the
disease relevance of candidate variants can be confirmed, by virtue of genetic complementation experiments.
At the same time, the standardized and scalable nature of our method renders it suitable to test potential
therapeutic interventions, e.g. to test the efficacy of potential gene-therapy, or to screen small molecule
libraries, while maintaining patient-specific granularity. The goal of this proposal is to apply our approach to an
expanded cohort of patient cells and to refine methods to interpret both genetic and pharmacological
perturbations. In this, I will be supported by an exceptional and multidisciplinary team of experts in clinical,
molecular and functional genetics, and computer scientists, within the world-class scientific environment
offered by Columbia University and the Broad Institute. In a carefully designed development plan, I will finalize
my training in machine learning and data science, expand my expertise to single-cell RNA-sequencing and
other single-cell methods, and acquire essential leadership and scholarly skills required for an independent
research career. Over the course of this award, I will apply our cellular profiling approach to generate a
standardized map of deep, quantitative descriptions of disease-associated cellular phenotypes across a
number of INs, MiDs and neurodegenerative conditions. We will explore the integration of RNA-sequencing to
enhance our approach. Finally, we will apply our method to the discovery and confirmation of new disease
genes, and screen a limited number of pharmacological interventions through our method. Together, the
proposed developmental plan and research strategy will foster my ability to lead an indep...

## Key facts

- **NIH application ID:** 10106181
- **Project number:** 1K99HG011488-01
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Wolfgang Maximilian Anton Pernice
- **Activity code:** K99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $97,780
- **Award type:** 1
- **Project period:** 2020-12-22 → 2022-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10106181

## Citation

> US National Institutes of Health, RePORTER application 10106181, Deep-learning based profiling of patient-derived cells as a tool for genomic and translational medicine (1K99HG011488-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10106181. Licensed CC0.

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