Mission

A single cell is the fundamental unit of life, and the collective actions of individual cells define the behavior of living organisms. In order to understand the identity and thus function of our cells, we must profile them individually. To this end, recent advances in the ability to sequence the molecular contents of a given cell (i.e., single cell RNA-seq, scRNA-seq) represent a transformative development. The unsupervised nature of scRNA- seq provides a new platform for discovery of transcriptomic cell states or developmental trajectories, with the potential to create a taxonomic classification of cells in the human body. However, a descriptive listing of cell types and their expressed genes represents only a first step; in order to truly understand cellular identity, we must understand the entire extent of intrinsic and extrinsic biological processes that regulate their behavior. Specifically, what is missing are approaches for simultaneously measuring the spectrum of intrinsic molecular constituents of a cell, from epigenetic state to transcriptional output to protein, and deriving the regulatory relationships between them, as well as elucidating the impact on these parameters of two key regulators of cellular function: spatial context (including cell-cell interactions) and lineage history.

We hypothesize that by collecting and integrating multiple pieces of information from single cells, we can achieve a complete picture of a cell’s phenotype and function in the context of normal function and disease. To this end, our “Center for Integrated Cellular Analysis” will develop critical technologies and computational methods for jointly analyzing multi-modal measurements within single cells.

Aims

Aim 1. Develop massively-parallel assays to simultaneously profile multiple molecular components across millions of cells. Cellular state and function are not driven by a single modality, but instead reflect the combined output of multiple molecular components. Here, we propose to extend our previously developed CITE- seq technology, which measures both surface proteins and mRNA levels in single cells, by substantially improving throughput, cost, and sample processing.

Aim 2. Identify the spatial and environmental determinants of cellular state in complex interacting populations. The spatial and environmental milieu of a cell modulates its cellular state from genome to protein, ultimately impacting its function. While this information is lost in standard single-cell sequencing workflows, here we propose to restore this context by integrating diverse data types including Spatial Transcriptomics, in-situ RNA-sequencing, and highly multiplexed immunofluorescence. We will develop a ‘spatially-aware’ statistical framework to analyze these data, and deep-learning tools to identify correspondences across experiments.

Aim 3. Develop scalable platforms to profile inherited molecular components, and determine the role of cell lineage in establishing molecular and phenotypic differences across cells. The origin of individual cells plays an essential role in determining cell-intrinsic states and function. Here, we will develop experimental and computational methods that capitalize on native lineage marks, both in the genome and epigenome, and determine their impact on cellular state and function.

Aim 4. Develop methods to harmonize single-cell profiles across distinct modalities, enabling the inference of cellular identity. We will develop new statistical approaches to mine, interpret, and integrate multiple modalities acquired from single cells, whether acquired simultaneously, or independently across several experiments. We will demonstrate how harmonized measurements can move beyond a ‘transcriptome-focused’ view of single cells, towards a unified representation of cell state based on multiple sources of information.