skip to main content

Title: Kit-Net: Self-Supervised Learning to Kit Novel 3D Objects into Novel 3D Cavities
Authors:
; ; ; ; ; ; ; ;
Award ID(s):
1734633
Publication Date:
NSF-PAR ID:
10314372
Journal Name:
Conference on Automation Science and Engineering (CASE) 2021
Sponsoring Org:
National Science Foundation
More Like this
  1. Cells in vivo generate mechanical traction on the surrounding 3D extracellular matrix (ECM) and neighboring cells. Such traction and biochemical cues may remodel the matrix, e.g., increase stiffness, which, in turn, influences cell functions and forces. This dynamic reciprocity mediates development and tumorigenesis. Currently, there is no method available to directly quantify single-cell forces and matrix remodeling in 3D. Here, we introduce a method to fulfill this long-standing need. We developed a high-resolution microfabricated sensor that hosts a 3D cell-ECM tissue formed by self-assembly. This sensor measures cell forces and tissue stiffness and can apply mechanical stimulation to the tissue. We measured single and multicellular force dynamics of fibroblasts (3T3), human colon (FET) and lung (A549) cancer cells, and cancer-associated fibroblasts (CAF05) with 1-nN resolution. Single cells show notable force fluctuations in 3D. FET/CAF coculture system, mimicking cancer tumor microenvironment, increased tissue stiffness by three times within 24 hours.