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It has been widely recognized that individual cells that exist within a large population of cells, even if they are genetically identical, can have divergent molecular makeups resulting from a variety of factors, including local environmental factors and stochastic processes within each cell. Presently, numerous approaches have been described that permit the resolution of these single-cell expression differences for RNA and protein; however, relatively few techniques exist for the study of lipids and metabolites in this manner. This study presents a methodology for the analysis of metabolite and lipid expression at the level of a single cell through the use of imaging mass spectrometry on a high-performance Fourier transform ion cyclotron resonance mass spectrometer. This report provides a detailed description of the overall experimental approach, including sample preparation as well as the data acquisition and analysis strategy for single cells. Applying this approach to the study of cultured RAW264.7 cells, we demonstrate that this method can be used to study the variation in molecular expression with cell populations and is sensitive to alterations in that expression that occurs upon lipopolysaccharide stimulation. Graphical Abstract

Imaging mass spectrometry (IMS) provides promising avenues to augment histopathological investigation with rich spatio-molecular information. We have previously developed a classification model to differentiate melanoma from nevi lesions based on IMS protein data, a task that is challenging solely by histopathologic evaluation. Most IMS-focused studies collect microscopy in tandem with IMS data, but this microscopy data is generally omitted in downstream data analysis. Microscopy, nevertheless, forms the basis for traditional histopathology and thus contains invaluable morphological information. In this work, we developed a multimodal classification pipeline that uses deep learning, in the form of a pre-trained artificial neural network, to extract the meaningful morphological features from histopathological images, and combine it with the IMS data. To test whether this deep learning-based classification strategy can improve on our previous results in classification of melanocytic neoplasia, we utilized MALDI IMS data with collected serial H&E stained sections for 331 patients, and compared this multimodal classification pipeline to classifiers using either exclusively microscopy or IMS data. The multimodal pipeline achieved the best performance, with ROC-AUCs of 0.968 vs. 0.938 vs. 0.931 for the multimodal, unimodal microscopy and unimodal IMS pipelines respectively. Due to the use of a pre-trained network to perform the morphological feature extraction, this pipeline does not require any training on large amounts of microscopy data. As such, this framework can be readily applied to improve classification performance in other experimental settings where microscopy data is acquired in tandem with IMS experiments.

Hepatic lipid accumulation, mainly in the form of triglycerides (TGs), is the hallmark of non-alcoholic fatty liver disease (NAFLD). To date, the spatial distribution of individual lipids in NAFLD-affected livers is not well characterized. This study aims to map the triglyceride distribution in normal human liver samples and livers with NAFLD and cirrhosis with imaging mass spectrometry (MALDI IMS). Specifically, whether individual triglyceride species differing by fatty acid chain length and degree of saturation correlate with the histopathological features of NAFLD as identified with classical H&E. Using a recently reported sodium-doped gold-assisted laser desorption/ionization IMS sample preparation, 20 human liver samples (five normal livers, five samples with simple steatosis, five samples with steatohepatitis, and five samples with cirrhosis) were analyzed at 10-μm lateral resolution. A total of 24 individual lipid species, primarily neutral lipids, were identified (22 TGs and two phospholipids). In samples with a low level of steatosis, TGs accumulated around the pericentral zone. In all samples, TGs with different degrees of side-chain saturation and side-chain length demonstrated differential distribution. Furthermore, hepatocytes containing macro lipid droplets were highly enriched in fully saturated triglycerides. This enrichment was also observed in areas of hepatocyte ballooning in samples with steatohepatitis and cirrhosis. In conclusion, macro lipid droplets in NAFLD are enriched in fully saturated triglycerides, indicating a possible increase in de novo lipogenesis that leads to steatohepatitis and cirrhosis.

The National Cancer Institute (NCI) supports numerous research consortia that rely on imaging technologies to study cancerous tissues. To foster collaboration and innovation in this field, the Image Analysis Working Group (IAWG) was created in 2019. As multiplexed imaging techniques grow in scale and complexity, more advanced computational methods are required beyond traditional approaches like segmentation and pixel intensity quantification. In 2022, the IAWG held a virtual hackathon focused on addressing challenges in analyzing complex, high‐dimensional datasets from fixed cancer tissues. The hackathon addressed key challenges in three areas: (1) cell type classification and assessment, (2) spatial data visualization and translation, and (3) scaling image analysis for large, multi‐terabyte datasets. Participants explored the limitations of current automated analysis tools, developed potential solutions, and made significant progress during the hackathon. Here we provide a summary of the efforts and resultant resources and highlight remaining challenges facing the research community as emerging technologies are integrated into diverse imaging modalities and data analysis platforms. Large multidimensional digital images of cancer tissue are becoming prolific, but many challenges exist to automatically extract relevant information from them using computational tools. We describe publicly available resources that have been developed jointly by expert and non‐expert computational biologists working together during a virtual hackathon to address several of these challenges, including artifact handling and image representation learning.

The Human BioMolecular Atlas Program (HuBMAP) aims to compile a Human Reference Atlas (HRA) for the healthy adult body at the cellular level. Functional tissue units (FTUs), relevant for HRA construction, are of pathobiological significance. Manual segmentation of FTUs does not scale; highly accurate and performant, open-source machine-learning algorithms are needed. We designed and hosted a Kaggle competition that focused on development of such algorithms and 1200 teams from 60 countries participated. We present the competition outcomes and an expanded analysis of the winning algorithms on additional kidney and colon tissue data, and conduct a pilot study to understand spatial location and density of FTUs across the kidney. The top algorithm from the competition, Tom, outperforms other algorithms in the expanded study, while using fewer computational resources. Tom was added to the HuBMAP infrastructure to run kidney FTU segmentation at scale—showcasing the value of Kaggle competitions for advancing research. Results from a Kaggle competition and expanded analysis of the winning algorithms are presented for segmentation of functional tissue units as part of the Human BioMolecular Atlas Program (HuBMAP).

The Human BioMolecular Atlas Program (HuBMAP) aims to create a multi-scale spatial atlas of the healthy human body at single-cell resolution by applying advanced technologies and disseminating resources to the community. As the HuBMAP moves past its first phase, creating ontologies, protocols and pipelines, this Perspective introduces the production phase: the generation of reference spatial maps of functional tissue units across many organs from diverse populations and the creation of mapping tools and infrastructure to advance biomedical research. The Human BioMolecular Atlas Program (HuBMAP) presents its production phase: the generation of spatial maps of functional tissue units across organs from diverse populations and the creation of tools and infrastructure to advance biomedical research.

Mucopolysaccharidosis type II (Hunter’s disease) mouse model (IdS-KO) was investigated by both imaging mass spectrometry (IMS) and immunohistochemistry (IHC) performed on the same tissue sections. For this purpose, IdS-KO mice brain sections were coated with sublimated 1,5-diaminonaphtalene and analyzed by high spatial resolution IMS (5 μm) and anti-GM3 IHC on the same tissue sections to characterize the ganglioside monosialated ganglioside (GM) deposits found in Hunter’s disease. IMS analysis have found that two species of GM3 and GM2 that are only different due to the length of their fatty acid residue (stearic or arachidic residue) were overexpressed in the IdS-KO mice compared to a control mouse. GM3 and GM2 were characterized by on-tissue exact mass and MS/MS compared to a GM3 standard. Realignment of both IMS and IHC data sets further confirmed the observed regioselective signal previously detected by providing direct correlation of the IMS image for the two GM3 overly expressed MS signals with the anti-GM3 IHC image. Furthermore, these regioselective GM MS signals were also found to have highly heterogeneous distributions within the GM3-IHC staining. Some deposits showed high content in GM3 and GM2 stearic species ( r  = 0.74) and others had more abundant GM3 and GM2 arachidic species ( r  = 0.76). Same-section analysis of Hunter’s disease mouse model by both high spatial resolution IMS and IHC provides a more in-depth analysis of the composition of the GM aggregates while providing spatial distribution of the observed molecular species. Graphical Abstract Ganglioside imaging mass spectrometry followed by immunohistochemistry performed on the same tissue section

Perturb-seq enabled the profiling of transcriptional effects of genetic perturbations in single cells but lacks the ability to examine the impact on tissue environments. We present Perturb-DBiT for simultaneous co-sequencing of spatial transcriptome and guide RNAs (gRNAs) on the same tissue section for in vivo CRISPR screen with genome-scale gRNA libraries, offering a comprehensive understanding of how genetic modifications affect cellular behavior and tissue architecture. This platform supports a variety of delivery vectors, gRNA library sizes, and tissue preparations, along with two distinct gRNA capture methods, making it adaptable to a wide range of experimental setups. In applying Perturb-DBiT, we conducted un-biased knockouts of tens of genes or at genome-wide scale across three cancer models. We mapped all gRNAs in individual colonies and corresponding transcriptomes in a human cancer metastatic colonization model, revealing clonal dynamics and cooperation. We also examined the effect of genetic perturbation on the tumor immune microenvironment in an immune-competent syngeneic model, uncovering differential and synergistic perturbations in promoting immune infiltration or suppression in tumors. Perturb-DBiT allows for simultaneously evaluating the impact of each knockout on tumor initiation, development, metastasis, histopathology, and immune landscape. Ultimately, it not only broadens the scope of genetic inquiry, but also lays the groundwork for developing targeted therapeutic strategies.

In many cancers, the establishment of a patient’s future treatment regime often relies on histopathological assessment of tumor tissue specimens in order to determine the extent of the ‘pathological response’ to a given therapy. However, histopathological assessment of pathological response remains subjective. Here we use MALDI mass spectrometry imaging to generate lipid signatures from colorectal cancer liver metastasis specimens resected from patients preoperatively treated with chemotherapy. Using these signatures we obtained a unique pathological response score that correlates with prognosis. In addition, we identify single lipid moieties that are overexpressed in different histopathological features of the tumor, which have potential as new biomarkers for assessing response to therapy. These data show that computational methods, focusing on the lipidome, can be used to determine prognostic markers for response to chemotherapy and may potentially improve risk assessment and patient care.