Multi-Contrast Computed Tomography Atlas of Healthy Pancreas

With the substantial diversity in population demographics, such as differences in age and body composition, the volumetric morphology of pancreas varies greatly, resulting in distinctive variations in shape and appearance. Such variations increase the difficulty at generalizing population-wide pancreas features. A volumetric spatial reference is needed to adapt the morphological variability for organ-specific analysis. Here, we proposed a high-resolution computed tomography (CT) atlas framework specifically optimized for the pancreas organ across multi-contrast CT. We introduce a deep learning-based pre-processing technique to extract the abdominal region of interests (ROIs) and leverage a hierarchical registration pipeline to align the pancreas anatomy across populations. Briefly, DEEDs affine and non-rigid registration are performed to transfer patient abdominal volumes to a fixed high-resolution atlas template. To generate and evaluate the pancreas atlas template, multi-contrast modality CT scans of 443 subjects (without reported history of pancreatic disease, age: 15-50 years old) are processed. Comparing with different registration state-of-the-art tools, the combination of DEEDs affine and non-rigid registration achieves the best performance for the pancreas label transfer across all contrast phases. We further perform external evaluation with another research cohort of 100 de-identified portal venous scans with 13 organs labeled, having the best label transfer performance of 0.504 Dice score in unsupervised setting. The qualitative representation (e.g., average mapping) of each phase creates a clear boundary of pancreas and its distinctive contrast appearance. The deformation surface renderings across scales (e.g., small to large volume) further illustrate the generalizability of the proposed atlas template

Tissue registration and exploration user interfaces in support of a human reference atlas

Seventeen international consortia are collaborating on a human reference atlas (HRA), a comprehensive, high-resolution, three-dimensional atlas of all the cells in the healthy human body. Laboratories around the world are collecting tissue specimens from donors varying in sex, age, ethnicity, and body mass index. However, harmonizing tissue data across 25 organs and more than 15 bulk and spatial single-cell assay types poses challenges. Here, we present software tools and user interfaces developed to spatially and semantically annotate ( register ) and explore the tissue data and the evolving HRA. A key part of these tools is a common coordinate framework, providing standard terminologies and data structures for describing specimen, biological structure, and spatial data linked to existing ontologies. As of April 22, 2022, the registration user interface has been used to harmonize and publish data on 5,909 tissue blocks collected by the Human Biomolecular Atlas Program (HuBMAP), the Stimulating Peripheral Activity to Relieve Conditions program (SPARC), the Human Cell Atlas (HCA), the Kidney Precision Medicine Project (KPMP), and the Genotype Tissue Expression project (GTEx). Further, 5,856 tissue sections were derived from 506 HuBMAP tissue blocks. The second exploration user interface enables consortia to evaluate data quality, explore tissue data spatially within the context of the HRA, and guide data acquisition. A companion website is at https://cns-iu.github.io/HRA-supporting-information/

The human body at cellular resolution: the NIH Human Biomolecular Atlas Program

Abstract: Transformative technologies are enabling the construction of three-dimensional maps of tissues with unprecedented spatial and molecular resolution. Over the next seven years, the NIH Common Fund Human Biomolecular Atlas Program (HuBMAP) intends to develop a widely accessible framework for comprehensively mapping the human body at single-cell resolution by supporting technology development, data acquisition, and detailed spatial mapping. HuBMAP will integrate its efforts with other funding agencies, programs, consortia, and the biomedical research community at large towards the shared vision of a comprehensive, accessible three-dimensional molecular and cellular atlas of the human body, in health and under various disease conditions

Effect of MALDI Matrices on Lipid Analyses of Biological Tissues Using MALDI-2 Post-Ionization Mass Spectrometry

Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) allows for highly multiplexed, untargeted detection of many hundreds of analytes from tissue. Recently, laser post-ionization (MALDI-2) has been developed for increased ion yield and sensitivity for lipid IMS. However, the dependence of MALDI-2 performance on the various lipid classes is largely unknown. To understand the effect of the applied matrix on MALDI-2 analysis of lipids, samples including an equimolar lipid standard mixture, various tissue homogenates, and intact rat kidney tissue sections were analyzed using the following matrices: α-cyano-4-hydroxycinnamic acid (CHCA), 2’,5’-dihydroxyacetophenone (DHA), 2’,5’-dihydroxybenzoic acid (DHB), and norharmane (NOR). Lipid signal enhancement of protonated species using MALDI-2 technology varied based on the matrix used. Although signal improvements were observed for all matrices, the most dramatic effects using MALDI-2 were observed using NOR and DHB. For lipid standards analyzed by MALDI-2, NOR provided the broadest coverage, enabling the detection of all 13 protonated standards, including non-polar lipids, whereas DHB gave less coverage but gave the highest signal increase for those lipids recorded. With respect to tissue homogenates and rat kidney tissue, mass spectra were compared and showed that the number and intensity of neutral lipids tentatively identified with MALDI-2 using NOR increased significantly (e.g. 5-fold intensity increase for triacylglycerol). In the cases of DHB with MALDI-2, the number of protonated lipids identified from tissue homogenates doubled with 152 on average compared to 76 with MALDI alone. High spatial resolution imaging (~20 µm) of rat kidney tissue showed similar results using DHB with 125 lipids tentatively identified from MALDI-2 spectra versus just 72 using standard MALDI. From the four matrices tested, NOR provided the greatest increase in sensitivity for neutral lipids (triacylglycerol, diacylglycerol, monoacylglycerol, cholesterol ester) and DHB provided the highest overall number of lipids detected using MALDI-2 technology. </p

Enhancement of Tryptic Peptide Signals from Tissue Sections using MALDI IMS Post-ionization (MALDI-2)

Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) allows for highly multiplexed, unlabeled mapping of analytes from tissue sections. However, further work is needed to improve sensitivity and depth of coverage for protein and peptide IMS. Laser-based post-ionization MALDI-2 has been shown to increase sensitivity for several molecular classes but thus far this has not been reported for peptides. Here, we demonstrate signal enhancement of proteolytic peptides from thin tissue sections of human kidney by conventional MALDI (termed MALDI-1), and conventional MALDI augmented using a second ionizing laser (termed MALDI-2). Proteins were digested in situ using trypsin prior to IMS analysis. For identification of peptides and proteins, a tissue homogenate was analyzed by LC-MS/MS for bottom-up proteomics and the corresponding proteins identified. These proteins were next fully ‘digested in silico’ to generate a database of theoretical peptides to then match to MALDI IMS datasets. Peptides were tentatively identified by matching the MALDI peak list to the database peptide list employing a 5 ppm error window. This resulted in 314 ± 45 (n=3) peptides and 1 112 ± 84 (n=3) peptides for MALDI-1 and MALDI-2, respectively. Protein identifications were similarly made by linking IMS data to the LC-MS/MS peptide database. With positive protein identifications requiring two or more peptides per protein, 55 ± 13 proteins were identified with MALDI-1 and 205 ± 10 with MALDI-2. These results demonstrate that MALDI-2 provides enhanced sensitivity for the spatial mapping of tryptic peptides and significantly increases the number of proteins identified in IMS experiments.<br /

Enhanced Sensitivity for High Spatial Resolution Lipid Analysis by Negative Ion Mode Matrix Assisted Laser Desorption Ionization Imaging Mass Spectrometry

We have achieved enhanced lipid imaging to a ∼10 μm spatial resolution using negative ion mode matrix assisted laser desorption ionization (MALDI) imaging mass spectrometry, sublimation of 2,5-dihydroxybenzoic acid as the MALDI matrix, and a sample preparation protocol that uses aqueous washes. We report on the effect of treating tissue sections by washing with volatile buffers at different pHs prior to negative ion mode lipid imaging. The results show that washing with ammonium formate, pH 6.4, or ammonium acetate, pH 6.7, significantly increases signal intensity and number of analytes recorded from adult mouse brain tissue sections. Major lipid species measured were glycerophosphoinositols, glycerophosphates, glycerolphosphoglycerols, glycerophosphoethanolamines, glycerophospho-serines, sulfatides, and gangliosides. Ion images from adult mouse brain sections that compare washed and unwashed sections are presented and show up to 5-fold increases in ion intensity for washed tissue. The sample preparation protocol has been found to be applicable across numerous organ types and significantly expands the number of lipid species detectable by imaging mass spectrometry at high spatial resolution