Abstract
“Brainless” cells, the living constituents inhabiting all biological materials, exhibit remarkably smart, i.e., stimuli-responsive and adaptive, behavior. The emergent spatial and temporal patterns of adaptation, observed as changes in cellular connectivity and tissue remodeling by cells, underpin neuroplasticity, muscle memory, immunological imprinting, and sentience itself, in diverse physiological systems from brain to bone. Connectomics addresses the direct connectivity of cells and cells’ adaptation to dynamic environments through manufacture of extracellular matrix, forming tissues and architectures comprising interacting organs and systems of organisms. There is imperative to understand the physical renderings of cellular experience throughout life, from the time of emergence, to growth, adaptation and aging-associated degeneration of tissues. Here we address this need through development of technological approaches that incorporate cross length scale (nm to m) structural data, acquired via multibeam scanning electron microscopy, with machine learning and information transfer using network modeling approaches. This pilot case study uses cutting edge imaging methods for nano- to meso-scale study of cellular inhabitants within human hip tissue resected during the normal course of hip replacement surgery. We discuss the technical approach and workflow and identify the resulting opportunities as well as pitfalls to avoid, delineating a path for cellular connectomics studies in diverse tissue/organ environments and their interactions within organisms and across species. Finally, we discuss the implications of the outlined approach for neuromechanics and the control of physical behavior and neuromuscular training.
Highlights
Cells of the human body populate their habitat through division, starting with two cells at conception and expanding to over 70 trillion cells over the course of a lifetime (Knothe Tate, 2017)
The manuscript proposes a paradigm shifting approach to understand the cellular underpinnings of diseases as different as osteoarthritis and early onset dementia in bone and brain
A pathological example of emergence would be disease emergence, e.g., of osteoarthritis in the musculoskeletal system or early onset dementia in the brain, which cannot be predicted based on the occurrence of a single sick cell but rather at the stage of loss in function or loss in return to homeostasis due to emergence of disease amongst groups of cells that interact
Summary
Cells of the human body populate their habitat through division, starting with two cells at conception and expanding to over 70 trillion cells over the course of a lifetime (Knothe Tate, 2017). We test machine learning algorithms with cellular network maps of the human hip to elucidate the role of cell networks in organ and organism (patho)physiology throughout life (Figure 1) This approach may pave the way for generation theranostics, i.e., enabling prediction of emergent cell scale pathology, including disease detection as well as treatment, well before permanent damage occurs at tissue and organ length scales. Multimodal imaging methods and assays using iodine to stain nuclear material demonstrate that better descriptors of cell health are needed (Anastopolous and Knothe Tate, 2021) With these limitations in mind, the technological approach provides novel opportunities for a new field of cellular epidemiology, where emergent changes in cell health may in the future be used to predict disease outbreaks and prevent disease transmission, much like they are used at the length scale of human inhabitants of geographically defined environments (Knothe Tate et al, 2016c; Dong et al, 2019).
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