High throughput measurement and perturbation of tissues and tissue-derived cellular models

Human tissues are composed of trillions of cells whose states and interactions drive health and disease. Deciphering which cells and interactions are associated with any given disease is challenging due to the vast complexity of a tissue. Successfully doing so requires a suite of tools for measuring and modeling tissues. First, tools for comprehensively measuring tissues– such as single-cell RNA-sequencing (scRNA-seq)- can identify the disruptions to the molecules, pathways, and cells in a tissue that are correlated with disease. Due to the unbiased nature of these profiling methods, this generates many hypotheses to test in order to identify the factors that cause disease. In vitro cellular models– such as patient derived organoids– that recapitulate tissue biology provide a platform for systematically testing these hypotheses. However, such experiments are difficult to scale, requiring the development of new technologies for perturbing complex model systems at scale. Here, we first demonstrate the value of comprehensively measuring tissues by applying scRNAseq to map the epithelial and immune correlates of disease in Zambian adults with Environmental Enteropathy (EE). In doing so, we reveal key aspects of the biology of this neglected disease, including, the presence of surface mucosal cells in EE, an increase in WNT/ßcatenin signaling in the EE epithelium, and a more cytotoxic phenotype in EE T cells. Through this work, we generate new hypotheses for therapeutic and nutritional intervention in EE. Next, we provide a new method for testing hypotheses in cellular models at scale by perturbing models with pooled perturbations whose effects we computationally deconvolute. We developed this “compressed screening” approach in the U2OS cell line with a high-content imaging (Cell Painting) readout and a bioactive small molecule perturbation library. We then applied this method to identify novel microenvironmental factors that modify RNA state in pancreatic ductal adenocarcinoma (PDAC) organoids. Altogether, the work in this thesis falls within a framework for understanding human biology by comprehensively measuring tissues to generate new hypotheses and then systematically testing these hypotheses by perturbing tissue-derived cellular models at scale. This framework provides a promising path for understanding human diseases and developing new therapeutics.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Computational and Systems Biology Program

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2022

Author dc:creator

• Kummerlowe, Conner Samuel

Advisor dc:contributor.advisor

• Shalek, Alex K.

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/147420

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/147420