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Towards Semantic Representations of Tissue Organization from High-Parameter Imaging Data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Towards Semantic Representations of Tissue Organization from High-Parameter Imaging Data./
作者:	Bhate, Salil Sanjay.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	235 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-07, Section: B.
Contained By:	Dissertations Abstracts International83-07B.
標題:	Decomposition. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28927416
ISBN:	9798762120302

Towards Semantic Representations of Tissue Organization from High-Parameter Imaging Data.
Bhate, Salil Sanjay.

Towards Semantic Representations of Tissue Organization from High-Parameter Imaging Data. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 235 p.

Source: Dissertations Abstracts International, Volume: 83-07, Section: B.

Thesis (Ph.D.)--Stanford University, 2021.

This item must not be sold to any third party vendors.

Viewed at a high spatial and molecular resolution, every individual instance of a biological tissue is unique. However, it is a tenet of evolution that prior evolutionary successes are adapted and repurposed for variations on function and for the creation of new functions. Thus, it would be expected that the organization characteristic of a tissue's type can be expressed in terms of a collection of repeated biological units that respect some rules governing their assembly and collective functionality. Advances in high-parameter imaging technologies present the opportunity to observe tissues at high spatial and molecular resolution. What, then, are the repeated biological units and the rules (governing the units' assembly and collective functionality), characterizing the organization of tissue types viewed through the lens of such technologies?We provide conceptual, mathematical and algorithmic tools towards possible answers of this question, and apply them to high-parameter tissue imaging data of lymphoid tissues as well as the immune-tumor microenvironment. The results yield clinically relevant insights into the specific biology of these tissues, as well as hint at general principles of tissue organization that become apparent only when tissues are assayed in such detail.We start considering cell types as the repeated units, describing those in lymphoid tissues with a disentangled representation learning strategy as well as in terms of cellular contacts. However, interpreting the outputs of a weakly-supervised neural network trained on such data suggested that specific regions of the spleen could be driving pathology in a murine model of autoimmunity. Urban neighborhoods (like the business district or residential area), are not merely regions, but instead have collective behaviors characteristic of cities. We therefore define cellular neighborhoods (CNs) and their collective behaviors (modular organization, functional states and communication) to describe tissues. While the CNs of the colorectal cancer (CRC) immune-tumor microenvironment (iTME) were conserved between high- and low-risk patient groups, their defined collective behaviors di↵ered in mechanistically and therapeutically suggestive ways. The utility of considering CNs as the repeated units, with which to express a tissue type's organization, was underscored by the observation that functional state of a granulocyte-enriched CN was associated with survival in the high-risk group, whereas the tissue-wide frequency of the PD-1+ CD4+ T cells, defining that functional state, was not.We introduce tissue schematics (TS), wherein a tissue type is described with respect to the rules governing the assembly of CNs into more complex structures termed tissue motifs (by analogy with DNA sequences), assumed to function through signal propagation between their constituent CNs. Using TS, we aligned the architectures of human lymphoid tissues, finding a core lymphoid motif that was specialized distinctly by the tonsil, lymph node and spleen. Developing a Markov-Chain Monte-Carlo approach to statistically quantifying the information in the TS of the CRC iTME, we identified a tissue motif (involving macrophage enriched, T cell enriched and vasculature enriched CNs) that likely arose by active processes counteracting entropy in the low-risk patient group.

ISBN: 9798762120302Subjects--Topical Terms:

3561186
Decomposition.

Towards Semantic Representations of Tissue Organization from High-Parameter Imaging Data.
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