Exploit Spatially Resolved Transcriptomic Data to Infer Cellular Features from Pathology Imaging Data.

Digital pathology is a rapidly advancing field where deep learning methods can be employed to extract meaningful imaging features. However, the efficacy of training deep learning models is often hindered by the scarcity of annotated pathology images, particularly images with detailed annotations for small image patches or tiles. To overcome this challenge, we propose an innovative approach that leverages paired spatially resolved transcriptomic data to annotate pathology images. We demonstrate the feasibility of this approach and introduce a novel transfer-learning neural network model, STpath (Spatial Transcriptomics and pathology images), designed to predict cell type proportions or classify tumor microenvironments. Our findings reveal that the features from pre-trained deep learning models are associated with cell type identities in pathology image patches. Evaluating STpath using three distinct breast cancer datasets, we observe its promising performance despite the limited training data. STpath excels in samples with variable cell type proportions and high-resolution pathology images. As the influx of spatially resolved transcriptomic data continues, we anticipate ongoing updates to STpath, evolving it into an invaluable AI tool for assisting pathologists in various diagnostic tasks.

Chimeric antigen receptor dendritic cells targeted delivery of a single tumoricidal factor for cancer immunotherapy.

BACKGROUND: Chimeric antigen receptor (CAR)-T cells have been used to treat blood cancers by producing a wide variety of cytokines. However, they are not effective in treating solid cancers and can cause severe side-effects, including cytokine release syndrome. TNFα is a tumoricidal cytokine, but it markedly increases the protein levels of cIAP1 and cIAP2, the members of inhibitor of apoptosis protein (IAP) family of E3 ubiquitin ligase that limits caspase-induced apoptosis. Degradation of IAP proteins by an IAP antagonist does not effectively kill cancer cells but enables TNFα to strongly induce cancer cell apoptosis. It would be a promising approach to treat cancers by targeted delivery of TNFα through an inactive adoptive cell in combination with an IAP antagonist. METHODS: Human dendritic cells (DCs) were engineered to express a single tumoricidal factor, TNFα, and a membrane-anchored Mucin1 antibody scFv, named Mucin 1 directed DCs expressing TNFα (M-DCsTNF). The efficacy of M-DCsTNF in recognizing and treating breast cancer was tested in vitro and in vivo. RESULTS: Mucin1 was highly expressed on the surface of a wide range of human breast cancer cell lines. M-DCsTNF directly associated with MDA-MB-231 cells in the bone of NSG mice. M-DCsTNF plus an IAP antagonist, SM-164, but neither alone, markedly induce MDA-MB-231 breast cancer cell apoptosis, which was blocked by TNF antibody. Importantly, M-DCsTNF combined with SM-164, but not SM-164 alone, inhibited the growth of patient-derived breast cancer in NSG mice. CONCLUSION: An adoptive cell targeting delivery of TNFα combined with an IAP antagonist is a novel effective approach to treat breast cancer and could be expanded to treat other solid cancers. Unlike CAR-T cell, this novel adoptive cell is not activated to produce a wide variety of cytokines, except for additional overexpressed TNF, and thus could avoid the severe side effects such as cytokine release syndrome.

Modeling children's moral development in postwar Taiwan through naturalistic observations preserved in historical texts.

A core issue in the interdisciplinary study of human morality is its ontogeny in diverse cultures, but systematic, naturalistic data in specific cultural contexts are rare to find. This study conducts a novel analysis of 213 children's socio-moral behavior in a historical, non-Western, rural setting, based on a unique dataset of naturalistic observations from the first field research on Han Chinese children. Using multilevel multinomial modeling, we examined a range of proactive behaviors in 0-to-12-year-old children's peer cooperation and conflict in an entire community in postwar Taiwan. We modeled the effects of age, sex, kinship, and behavioral roles, and revealed complex interactions between these four variables in shaping children's moral development. We discovered linkages between coercive and non-coercive behaviors as children strategically negotiated leadership dynamics. We identified connections between prosocial and aggressive behaviors, illuminating the nuances of morality in real life. Our analysis also revealed gendered patterns and age-related trends that deviated from cultural norms and contradicted popular assumptions about Chinese family values. These findings highlight the importance of naturalistic observations in cultural contexts for understanding how we become moral persons. This re-analysis of historically significant fieldnotes also enriches the interdisciplinary study of child development across societies.