# Omics information maximization in single-cell sequencing with hybrid molecular and computational approaches

> **NIH NIH R35** · STANFORD UNIVERSITY · 2023 · $471,022

## Abstract

ABSTRACT
The overall goal of the proposed research program is to improve our understanding of single cell biology through
information maximization techniques, by applying molecular engineering and computational approaches in
sequencing. Specifically, single cell sequencing is rapidly becoming the predominant method for studying human
biology and disease because it removes the confounding factor of sequencing cell mixtures in bulk. However, it
has major pitfalls: significant material consumption during library preparation, noisy data readouts and signal
dropout, and unclear paths for data integration across datasets.
The overall vision of the proposed research program is to develop a pan-omic analysis strategy that enables
perpetual re-use of any single cell source material. It revolves around a hybrid molecular engineering and
computational framework that is loosely inspired by principles found in computing. The experimental core of the
proposed research program revolved around a new molecular technology referred to as APEX (‘Attachment-
based Primer EXtension’). The major innovation of APEX is the covalent conjugation of genomic material (i.e.
DNA or cDNA) to a solid phase support such as an agarose magnetic bead, followed by utilizing only polymerase-
based assays for non-destructive molecular interrogation.
In this project, we will focus APEX development on single cell transcriptome sequencing applications, with
general applicability to genome biology. As a model system, we will utilize peripheral lymphocytes as they consist
of complex subpopulations with distinct characteristics at multiple levels of omic features. The project will focus
on assay development and optimization, development of bioinformatic algorithms for data integration, and scale
up to large cohorts as a demonstration of the scalability of the technology.

## Key facts

- **NIH application ID:** 10657366
- **Project number:** 5R35HG011292-04
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Billy Tsz Cheong Lau
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $471,022
- **Award type:** 5
- **Project period:** 2020-09-01 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10657366

## Citation

> US National Institutes of Health, RePORTER application 10657366, Omics information maximization in single-cell sequencing with hybrid molecular and computational approaches (5R35HG011292-04). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10657366. Licensed CC0.

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