Wang, C., et al. Deep Learning-based Subcellular Phenotyping of Protrusion Dynamics Reveals Fine Differential Drug Responses at Subcellular and Single-Cell Levels, bioRxiv, 2022

We develop a deep learning-based framework "DeepHACKS" for subcellular phenotyping of protrusion dynamics to reveal fine differential drug responses at subcellular and single-Cell Levels.

Bartsch, Y.C.*, Wang, C.*, et al. Humoral signatures of protective and pathological SARS-CoV2 infection in children. Nature Medicine

Using Systems Serology, here we observed in 25 children with acute mild COVID a functional phagocyte and complement activating IgG response to SARS-CoV-2, comparable to the acute responses generated in adults with mild disease.

Zohar, T., Loos, C., Fischinger, S., Atyeo, C., Wang, C., et al. Compromised Humoral Functional Evolution Tracks with SARS-CoV-2 Mortality. Cell.

We highlight distinct humoral trajectories associated with resolution of SARS-CoV-2 infection and the need for early functional humoral immunity.

Wang, C.*, Choi, H.J.*, et al. HACKS (deconvolution of heterogeneous activity in coordination of cytoskeleton at the subcellular level), Nature Communication.

We establish a computational framework called HACKS (deconvolution of heterogeneous activity in coordination of cytoskeleton at the subcellular level) to deconvolve the subcellular heterogeneity of lamellipodial protrusion from live cell imaging.

Kim, S.*, Wang, C.*, et al. Deep transfer learning for Lens-free digital in-line holography (LDIH) images in the context of cellular analyses. Scientific Reports.

We describe a deep transfer learning (DTL) based approach to process LDIH images in the context of cellular analyses.

Wang, C., Zhang, X., et al. vU-net: accurate cell edge segmentation in time-lapse fluorescence live cell images based on convolutional neural network, bioRxiv.

We propose a novel framework called vU-net to reconstruct cell edge with a higher accuracy using limited training images.

Wang, C., Kang, S., et al. Edge Detection of Cryptic Lamellipodia Assisted by Deep Learning, bioRxiv.

By combining Canny edge detector, Convolutional Neural Networks (CNNs), and local intensity thresholding, we were able to detect cryptic lamellipodial edges of submarginal cells with high accuracy from the fluorescence time-lapse movies.