Biology subjects

Chang, Y. H.

Publications and source records attributed to Chang, Y. H..

41 records · Page 3Linked to original sources

bioRxiv2019

RESTORE: Robust intEnSiTy nORmalization mEthod for Multiplexed Imaging

Recent advances in multiplexed imaging technologies promise to improve the understanding of the functional states of individual cells and the interactions between the cells in tissues. This often requires compilation of results from multiple samples. However, quantitative integration of information between samples is complicated by variations in staining intensity and background fluorescence that obscure biological variations. Failure to remove these unwanted artefacts will complicate downstream analysis and diminish the value of multiplexed imaging for clinical applications. Here, to compensate for unwanted variations, we automatically identify negative control cells for each marker within the same tissue and use their expression levels to infer background signal level. The intensity profile is normalized by the inferred level of the negative control cells to remove between-sample variation. Using a tissue microarray data and a pair of longitudinal biopsies sample, we demonstrated that the proposed approach can remove unwanted variations effectively and shows robust performance.

systems biology↗

bioRxiv2019

SHIFT: speedy histological-to-immunofluorescent translation of whole slide images enabled by deep learning

Spatially-resolved molecular profiling by immunostaining tissue sections is a key feature in cancer diagnosis, subtyping, and treatment, where it complements routine histopathological evaluation by clarifying tumor phenotypes. In this work, we present a deep learning-based method called speedy histological-to-immunofluorescent translation (SHIFT) which takes histologic images of hematoxylin and eosin-stained tissue as input, then in near-real time returns inferred virtual immunofluorescence (IF) images that accurately depict the underlying distribution of phenotypes without requiring immunostaining of the tissue being tested. We show that deep learning-extracted feature representations of histological images can guide representative sample selection, which improves SHIFT generalizability. SHIFT could serve as an efficient preliminary, auxiliary, or substitute for IF by delivering multiplexed virtual IF images for a fraction of the cost and in a fraction of the time required by nascent multiplexed imaging technologies.\n\nKEY POINTSO_LISpatially-resolved molecular profiling is an essential complement to histopathological evaluation of cancer tissues.\nC_LIO_LIInformation obtained by immunofluorescence imaging is encoded by features in histological images.\nC_LIO_LISHIFT leverages previously unappreciated features in histological images to facilitate virtual immunofluorescence staining.\nC_LIO_LIFeature representations of images guide sample selection, improving model generalizability.\nC_LI

systems biology↗

bioRxiv2019

Predicting Primary Site of Secondary Liver Cancer with a Neural Estimator of Metastatic Origin (NEMO)

Pathologists rely on clinical information, tissue morphology, and sophisticated molecular diagnostics to accurately infer the metastatic origin of secondary liver cancer. In this paper, we introduce a deep learning approach to identify spatially localized regions of cancerous tumor within hematoxylin and eosin stained tissue sections of liver cancer and to generate predictions of the cancers metastatic origin. Our approach achieves an accuracy of 90.2% when classifying metastatic origin of whole slide images into three distinct classes, which compares favorably to an established clinical benchmark by three board-certified pathologists whose accuracies ranged from 90.2% to 94.1% on the same prediction task. This approach illustrates the potential impact of deep learning systems to leverage morphological and structural features of H&E stained tissue sections to guide pathological and clinical determination of the metastatic origin of secondary liver cancers.

cancer biology↗

bioRxiv2019

HER2 Cancer Protrusion Growth Signaling Regulated by Unhindered, Localized Filopodial Dynamics

Protrusions are plasma membrane extensions that are found in almost every cell in the human body. Cancer cell filopodial and lamellipodial protrusions play key roles in the integral processes of cell motility and signaling underlying tumor invasion and metastasis. HER2 (ErbB-2) is overexpressed in diverse types of tumors and regulates PI3K-pathway-mediated protrusion growth. It is known that HER2 resides at breast cancer cell protrusions, but how protrusion-based HER2 spatiotemporal dynamics shape cancer signaling is unclear. Here, we study how HER2 location and motion regulate protrusion signaling and growth using quantitative spatio-temporal molecular imaging approaches. Our data highlight morphologically-segregated features of filopodial and lamellipodial protrusions, in in vitro 2D breast cancer cells and in vivo intact breast tumor. Functional-segregation parallels morphological-segregation, as HER2 and its activated downstream pAKT-PI3K signaling remain spatially-localized at protrusions, provoking new protrusion growth proximal to sites of HER2 activation. HER2 in SKBR3 breast cancer cell filopodia exhibits fast, linearly-directed motion that is distinct from lamellipodia and non-protrusion subcellular regions ([~]3-4 times greater diffusion constant, rapid speeds of 2-3 um2/s). Surprisingly, filopodial HER2 motion is passive, requiring no active energy sources. Moreover, while HER2 motion in lamellipodia and non-protrusion regions show hindered diffusion typical of membrane proteins, HER2 diffuses freely within filopodia. We conclude that HER2 activation, propagation, and functional protrusion growth is a local process in which filopodia have evolved to exploit Brownian thermal fluctuations within a barrier-free nanostructure to transduce rapid signaling. These results support the importance of developing filopodia and other protrusion-targeted strategies for cancer.

cancer biology↗

bioRxiv2019

High-throughput single-particle tracking reveals nested membrane nanodomain organization that dictates Ras diffusion and trafficking

Membrane nanodomains have been implicated in Ras signaling, but what these domains are and how they interact with Ras remain obscure. Using high throughput single particle tracking with photoactivated localization microscopy and detailed trajectory analysis, here we show that distinct membrane domains dictate KRas diffusion and trafficking in U2OS cells. KRas exhibits an immobile state in domains [~]70 nm in size, each embedded in a larger domain ([~]200 nm) that confers intermediate mobility, while the rest of the membrane supports fast diffusion. Moreover, KRas is continuously removed from the membrane via the immobile state and replenished to the fast state, likely coupled to internalization and recycling. Importantly, both the diffusion and trafficking properties of KRas remain invariant over a broad range of protein expression levels. Our results reveal how membrane organization dictates KRas diffusion and trafficking and offer insight into how Ras signaling may be regulated through spatial mechanisms.

biophysics↗