Identification of B cell subsets based on antigen receptor sequences using deep learning.

B cell receptors (BCRs) denote antigen specificity, while corresponding cell subsets indicate B cell functionality. Since each B cell uniquely encodes this combination, physical isolation and subsequent processing of individual B cells become indispensable to identify both attributes. However, this approach accompanies high costs and inevitable information loss, hindering high-throughput investigation of B cell populations. Here, we present BCR-SORT, a deep learning model that predicts cell subsets from their corresponding BCR sequences by leveraging B cell activation and maturation signatures encoded within BCR sequences. Subsequently, BCR-SORT is demonstrated to improve reconstruction of BCR phylogenetic trees, and reproduce results consistent with those verified using physical isolation-based methods or prior knowledge. Notably, when applied to BCR sequences from COVID-19 vaccine recipients, it revealed inter-individual heterogeneity of evolutionary trajectories towards Omicron-binding memory B cells. Overall, BCR-SORT offers great potential to improve our understanding of B cell responses.

Hema-seq reveals genomic aberrations in a rare simultaneous occurrence of hematological malignancies.

Co-occurrence of multiple myeloma and acute myelogenous leukemia is rare, with both malignancies often tracing back to multipotent hematopoietic stem cells. Cytogenetic techniques are the established baseline for diagnosis and characterization of complex hematological malignancies. In this study, we develop a workflow called Hema-seq to delineate clonal changes across various hematopoietic lineages through the integration of whole-genome sequencing, copy-number variations, cell morphology, and cytogenetic aberrations. In Hema-seq, cells are selected from Wright-stained slides and fluorescent probe-stained slides for sequencing. This technique therefore enables direct linking of whole-genome sequences to cytogenetic profiles. Through this method, we mapped sequential clonal alterations within the hematopoietic lineage, identifying critical shifts leading to myeloma and acute myeloid leukemia (AML) cell formations. By synthesizing data from each cell lineage, we provided insights into the hematopoietic tree's clonal evolution. Overall, this study highlights Hema-seq's capability in deciphering genomic heterogeneity in complex hematological malignancies, which can enable better diagnosis and treatment strategies.

Barcoded multiple displacement amplification for high coverage sequencing in spatial genomics.

Determining mutational landscapes in a spatial context is essential for understanding genetically heterogeneous cell microniches. Current approaches, such as Multiple Displacement Amplification (MDA), offer high genome coverage but limited multiplexing, which hinders large-scale spatial genomic studies. Here, we introduce barcoded MDA (bMDA), a technique that achieves high-coverage genomic analysis of low-input DNA while enhancing the multiplexing capabilities. By incorporating cell barcodes during MDA, bMDA streamlines library preparation in one pot, thereby overcoming a key bottleneck in spatial genomics. We apply bMDA to the integrative spatial analysis of triple-negative breast cancer tissues by examining copy number alterations, single nucleotide variations, structural variations, and kataegis signatures for each spatial microniche. This enables the assessment of subclonal evolutionary relationships within a spatial context. Therefore, bMDA has emerged as a scalable technology with the potential to advance the field of spatial genomics significantly.

Pen-drawn Marangoni swimmer.

Pen-drawing is an intuitive, convenient, and creative fabrication method for delivering emergent and adaptive design to real devices. To demonstrate the application of pen-drawing to robot construction, we developed pen-drawn Marangoni swimmers that perform complex programmed tasks using a simple and accessible manufacturing process. By simply drawing on substrates using ink-based Marangoni fuel, the swimmers demonstrate advanced robotic motions such as polygon and star-shaped trajectories, and navigate through maze. The versatility of pen-drawing allows the integration of the swimmers with time-varying substrates, enabling multi-step motion tasks such as cargo delivery and return to the original place. We believe that our pen-based approach will significantly expand the potential applications of miniaturized swimming robots and provide new opportunities for simple robotic implementations.

Derivation of prognostic contextual histopathological features from whole-slide images of tumours via graph deep learning.

Methods of computational pathology applied to the analysis of whole-slide images (WSIs) do not typically consider histopathological features from the tumour microenvironment. Here, we show that a graph deep neural network that considers such contextual features in gigapixel-sized WSIs in a semi-supervised manner can provide interpretable prognostic biomarkers. We designed a neural-network model that leverages attention techniques to learn features of the heterogeneous tumour microenvironment from memory-efficient representations of aggregates of highly correlated image patches. We trained the model with WSIs of kidney, breast, lung and uterine cancers and validated it by predicting the prognosis of 3,950 patients with these four different types of cancer. We also show that the model provides interpretable contextual features of clear cell renal cell carcinoma that allowed for the risk-based retrospective stratification of 1,333 patients. Deep graph neural networks that derive contextual histopathological features from WSIs may aid diagnostic and prognostic tasks.

Spatial epitranscriptomics reveals A-to-I editome specific to cancer stem cell microniches.

Epitranscriptomic features, such as single-base RNA editing, are sources of transcript diversity in cancer, but little is understood in terms of their spatial context in the tumour microenvironment. Here, we introduce spatial-histopathological examination-linked epitranscriptomics converged to transcriptomics with sequencing (Select-seq), which isolates regions of interest from immunofluorescence-stained tissue and obtains transcriptomic and epitranscriptomic data. With Select-seq, we analyse the cancer stem cell-like microniches in relation to the tumour microenvironment of triple-negative breast cancer patients. We identify alternative splice variants, perform complementarity-determining region analysis of infiltrating T cells and B cells, and assess adenosine-to-inosine base editing in tumour tissue sections. Especially, in triple-negative breast cancer microniches, adenosine-to-inosine editome specific to different microniche groups is identified.

Color-Coded Droplets and Microscopic Image Analysis for Multiplexed Antibiotic Susceptibility Testing.

Since the discovery of antibiotics, the emergence of antibiotic resistance has become a global issue that is threatening society. In the era of antibiotic resistance, finding the proper antibiotics through antibiotic susceptibility testing (AST) is crucial in clinical settings. However, the current clinical process of AST based on the broth microdilution test has limitations on scalability to expand the number of antibiotics that are tested with various concentrations. Here, we used color-coded droplets to expand the multiplexing of AST regarding the kind and concentration of antibiotics. Color type and density differentiate the kind of antibiotics and concentration, respectively. Microscopic images of a large view field contain numbers of droplets with different testing conditions. Image processing analysis detects each droplet, decodes color codes, and measures the bacterial growth in the droplet. Testing E. coli ATCC 25922 with ampicillin, gentamicin, and tetracycline shows that the system can provide a robust and scalable platform for multiplexed AST. Furthermore, the system can be applied to various drug testing systems, which require several different testing conditions.

Effect of proinflammatory cytokines on the expression and regulation of human beta-defensin 2 in human dental pulp cells.

INTRODUCTION: Although the expression of human beta-defensin-2 (hBD-2) in odontoblasts from human dental pulp (HDP) has been reported, the production of hBD-2 and its regulation remains poorly understood. The aim of this study was to investigate the effect of cytokines on the induction of hBD-2 and its signaling mechanisms in HDP cells. METHODS: After stimulation with tumor necrosis factor alpha (TNF-alpha) and interleukin 1 alpha (IL-1 alpha), reverse-transcriptase polymerase chain reaction, Western blot, and enzyme-linked immunosorbent assay experiments were performed to evaluate the effects of these cytokines on the production of hBD-2. RESULTS: TNF-alpha and IL-1 alpha synergistically increased hBD-2 messenger RNA levels, protein expression, and activity. The up-regulation of hBD-2 by cytokines was attenuated by pretreatment with inhibitors of PKC, JNK, p38, ERK MAPK, nuclear factor-kappaB, and adenosine monophosphate-activated protein kinase (AMPK). CONCLUSION: These results suggest that TNF-alpha and IL-1 alpha up-regulate HBD-2 expression in HDP cells through the PKC, JNK MAPK, p38, ERK, NF-kappaB, and AMPK pathways. Thus, the induction of hBD-2 by proinflammatory cytokines might up-regulate the pulpal host immune defense system.