Selecting and isolating various cell types is a critical procedure in many applications,
including immune therapy, regenerative medicine, and cancer research. Usually, these
selection processes involve some labeling or another invasive step potentially affecting
cellular functionality or damaging the cell. In the current proof of principle study,
we first introduce an optical biosensor-based method capable of classification between
healthy and numerous cancerous cell types in a label-free setup. We present high classification
accuracy based on the monitored single-cell adhesion kinetic signals. We developed
a high-throughput data processing pipeline to build a benchmark database of ~ 4500
single-cell adhesion measurements of a normal preosteoblast (MC3T3-E1) and various
cancer (HeLa, LCLC-103H, MDA-MB-231, MCF-7) cell types. Several datasets were used
with different cell-type selections to test the performance of deep learning-based
classification models, reaching above 70–80% depending on the classification task.
Beyond testing these models, we aimed to draw interpretable biological insights from
their results; thus, we applied a deep neural network visualization method (grad-CAM)
to reveal the basis on which these complex models made their decisions. Our proof-of-concept
work demonstrated the success of a deep neural network using merely label-free adhesion
kinetic data to classify single mammalian cells into different cell types. We propose
our method for label-free single-cell profiling and in vitro cancer research involving
adhesion. The employed label-free measurement is noninvasive and does not affect cellular
functionality. Therefore, it could also be adapted for applications where the selected
cells need further processing, such as immune therapy and regenerative medicine.
