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Deep Learning-Enabled Integrated Measurements of Immune Signaling in Primary Human Macrophages


Laubscher, Emily Chiu (2024) Deep Learning-Enabled Integrated Measurements of Immune Signaling in Primary Human Macrophages. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/hayp-kx45.


Examination of biological systems at the single-cell level reveals heterogeneity in both time and space. Single-cell temporal and spatial heterogeneity allow communities of cells to process noisy stimuli and perform complex tasks. We leveraged state-of-the-art imaging technologies to characterize cell-to-cell heterogeneity in responses to environmental stimuli to reveal mechanisms of information transmission. Fluorescent live-cell reporters enable real-time visualization of the activity state of cell signaling proteins. Signaling dynamics allow cells to translate information about environmental stimuli into cellular behaviors. Chapter 2 explores the variety of live-cell reporters designed to characterize the dynamic patterns of activity of key signaling pathways, and covers the development of two live-cell reporters. Spatial transcriptomics assays, on the other hand, excel at capturing heterogeneity in spatial gene expression patterns, which is often required to enable a tissue to perform complex functions. Chapter 3 details the development of Polaris, a deep learning-enabled analysis method for spatial transcriptomics data. Polaris is an assay-agnostic, turnkey solution for analyzing images from spatial transcriptomics experiments, minimizing the time and expertise require to extract biological insights. In chapter 4, we pair dynamic measurements of live-cell reporters with a spatial transcriptomics measurement in an integrated imaging assay in primary human macrophages. This imaging assay revealed transcriptional sub-populations of cells with differing distributions of dynamic immune signaling responses and morphological states.

This work contributes a number of methodological developments, including live- cell reporter expression in primary human macrophages and deep learning-enabled spatial transcriptomics image analysis. Expression of live-cell reporters in primary macrophages will enable the investigation of environmental cues shape macrophages’ cell state, which is highly plastic and shaped by external stimuli. Polaris expedites the analysis of this multi-modal imaging data set, extracting single-cell gene expression values without manual parameter tuning. However, Polaris’ impact extends beyond the scope of this work to the broader spatial biology field as its spot detection and gene decoding capabilities generalize to data sets from a variety of sample types and imaging modalities. Finally, our paired dynamics-spatial transcriptomics imaging assay can be generally applied to characterize information transmission from environmental stimuli through signaling dynamics to the expression of downstream genes for a wide variety of signaling pathways in primary and immortalized cell types.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:macrophages, immune signaling, spatial transcriptomics, deep learning, image analysis
Degree Grantor:California Institute of Technology
Division:Chemistry and Chemical Engineering
Major Option:Chemistry
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Van Valen, David A.
Thesis Committee:
  • Wei, Lu (chair)
  • Chong, Shasha
  • Rothenberg, Ellen V.
  • Van Valen, David A.
Defense Date:7 May 2024
Funding AgencyGrant Number
Howard Hughes Medical InstituteUNSPECIFIED
Heritage Medical Research InstituteUNSPECIFIED
Shurl and Kay Curci FoundationUNSPECIFIED
Gordon and Betty Moore FoundationUNSPECIFIED
Record Number:CaltechTHESIS:05222024-204650454
Persistent URL:
Related URLs:
URLURL TypeDescription adapted for Ch. 3
Laubscher, Emily Chiu0009-0008-0242-0507
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:16412
Deposited By: Emily Laubscher
Deposited On:28 May 2024 22:18
Last Modified:04 Jun 2024 18:45

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