# MULTIseq: multiplexing massively parallel single cell transcriptional analysis across time, space, and conditions

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2022 · $318,963

## Abstract

Abstract
Many human diseases – including autoimmunity, cancer, and aging – are linked to a breakdown in the ability of
different cell types to coordinate their behaviors and decisions across a tissue. Coordination occurs through a
variety of mechanisms, including molecular, mechanical, and electrical signals that are exchanged among cells.
These signals are integrated through signaling pathways, and ultimately, impact the transcription of numerous
genes. The multicellular regulatory networks that coordinate transcription among each cell type of a tissue can
be deduced by perturbing the transcriptional state of individual genes or cell types, then measuring how each
cell type relaxes to a new steady state, both dynamically and spatially. However, the systematic application of
this approach will require new experimental tools. We will therefore build on a new method developed in our lab
– MULTIseq – that allows the simultaneous transcriptional analysis of numerous samples using existing single
cell RNAseq pipelines (e.g. DropSeq, 10X, SeqWell, etc.). MULTIseq enables the implementation of entirely new
classes of single cell experiments. The method has the additional benefits of dramatically increasing throughput
and decreasing artifacts such as doublets and batch effects. As a consequence, MULTIseq allows researchers
to gather richer single cell information at a 5- to 100-fold reduction in sample preparation costs. We present three
aims that will enable application of MULTIseq to analyze dynamic biological processes in time; heterogeneous
biological processes in space; and the response of complex tissues to hundreds or thousands of genetic,
chemical, or microenvironmental perturbations. Finally, we propose to combine MULTIseq with methods to
reduce sequencing costs by 10-fold while simultaneously increasing the information content from low-abundance
transcripts. Completion of our goals will provide a powerful new tool to the scientific community that can be
applied to any cell type using a simple and economical protocol.

## Key facts

- **NIH application ID:** 10439633
- **Project number:** 5R01GM135462-04
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Zev Jordan Gartner
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $318,963
- **Award type:** 5
- **Project period:** 2019-09-20 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10439633

## Citation

> US National Institutes of Health, RePORTER application 10439633, MULTIseq: multiplexing massively parallel single cell transcriptional analysis across time, space, and conditions (5R01GM135462-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10439633. Licensed CC0.

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