Multimodal Imaging Mass Spectrometry: Next Generation Molecular Mapping in Biology and Medicine.

Imaging mass spectrometry has become a mature molecular mapping technology that is used for molecular discovery in many medical and biological systems. While powerful by itself, imaging mass spectrometry can be complemented by the addition of other orthogonal, chemically informative imaging technologies to maximize the information gained from a single experiment and enable deeper understanding of biological processes. Within this review, we describe MALDI, SIMS, and DESI imaging mass spectrometric technologies and how these have been integrated with other analytical modalities such as microscopy, transcriptomics, spectroscopy, and electrochemistry in a field termed multimodal imaging. We explore the future of this field and discuss forthcoming developments that will bring new insights to help unravel the molecular complexities of biological systems, from single cells to functional tissue structures and organs

Referenced Kendrick Mass Defect Annotation and Class-Based Filtering of Imaging MS Lipidomics Experiments.

Because of their diverse functionalities in cells, lipids are of primary importance when characterizing molecular profiles of physiological and disease states. Imaging mass spectrometry (IMS) provides the spatial distributions of lipid populations in tissues. Referenced Kendrick mass defect (RKMD) analysis is an effective mass spectrometry (MS) data analysis tool for classification and annotation of lipids. Herein, we extend the capabilities of RKMD analysis and demonstrate an integrated method for lipid annotation and chemical structure-based filtering for IMS datasets. Annotation of lipid features with lipid molecular class, radyl carbon chain length, and degree of unsaturation allows image reconstruction and visualization based on each structural characteristic. We show a proof-of-concept application of the method to a computationally generated IMS dataset and validate that the RKMD method is highly specific for lipid components in the presence of confounding background ions. Moreover, we demonstrate an application of the RKMD-based annotation and filtering to matrix-assisted laser desorption/ionization (MALDI) IMS lipidomic data from human kidney tissue analysis

Integrating ion mobility and imaging mass spectrometry for comprehensive analysis of biological tissues: A brief review and perspective.

Imaging mass spectrometry (IMS) technologies are capable of mapping a wide array of biomolecules in diverse cellular and tissue environments. IMS has emerged as an essential tool for providing spatially targeted molecular information due to its high sensitivity, wide molecular coverage, and chemical specificity. One of the major challenges for mapping the complex cellular milieu is the presence of many isomers and isobars in these samples. This challenge is traditionally addressed using orthogonal liquid chromatography (LC)-based analysis, though, common approaches such as chromatography and electrophoresis are not able to be performed at timescales that are compatible with most imaging applications. Ion mobility offers rapid, gas-phase separations that are readily integrated with IMS workflows in order to provide additional data dimensionality that can improve signal-to-noise, dynamic range, and specificity. Here, we highlight recent examples of ion mobility coupled to IMS and highlight their importance to the field

High Spatial Resolution MALDI Imaging Mass Spectrometry of Fresh-Frozen Bone.

Bone and bone marrow are vital to mammalian structure, movement, and immunity. These tissues are also commonly subjected to molecular alterations giving rise to debilitating diseases like rheumatoid arthritis and osteomyelitis. Technologies such as matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) facilitate the discovery of spatially resolved chemical information in biological tissue samples to help elucidate the complex molecular processes underlying pathology. Traditionally, preparation of osseous tissue for MALDI IMS has been difficult due to its mineralized composition and heterogeneous morphology, and compensation for these challenges with decalcification and fixation protocols can remove or delocalize molecular species. Here, sample preparation methods were advanced to enable multimodal MALDI IMS of undecalcified, fresh-frozen murine femurs, allowing the distribution of endogenous lipids to be linked to tissue structures and cell types. Adhesive-bound bone sections were mounted onto conductive glass slides with microscopy-compatible glue and freeze-dried to minimize artificial bone marrow damage. High spatial resolution (10 μm) MALDI IMS was employed to characterize lipid distributions, and use of complementary microscopy modalities aided tissue and cell assignments. For example, various phosphatidylcholines localize to the bone marrow, adipose tissue, marrow adipose tissue, and muscle. Further, sphingomyelin(42:1) was abundant in megakaryocytes, whereas sphingomyelin(42:2) was diminished in this cell type. These data reflect the vast molecular and cellular heterogeneity indicative of the bone marrow and the soft tissue surrounding the femur. Multimodal MALDI IMS has the potential to advance bone-related biomedical research by offering deep molecular coverage with spatial relevance in a preserved native bone microenvironment

Highly multiplexed immunofluorescence of the human kidney using co-detection by indexing.

The human kidney is composed of many cell types that vary in their abundance and distribution from normal to diseased organ. As these cell types perform unique and essential functions, it is important to confidently label each within a single tissue to accurately assess tissue architecture and microenvironments. Towards this goal, we demonstrate the use of co-detection by indexing (CODEX) multiplexed immunofluorescence for visualizing 23 antigens within the human kidney. Using CODEX, many of the major cell types and substructures, such as collecting ducts, glomeruli, and thick ascending limb, were visualized within a single tissue section. Of these antibodies, 19 were conjugated in-house, demonstrating the flexibility and utility of this approach for studying the human kidney using custom and commercially available antibodies. We performed a pilot study that compared both fresh frozen and formalin-fixed paraffin-embedded healthy non-neoplastic and diabetic nephropathy kidney tissues. The largest cellular differences between the two groups was observed in cells labeled with aquaporin 1, cytokeratin 7, and α-smooth muscle actin. Thus, our data show the power of CODEX multiplexed immunofluorescence for surveying the cellular diversity of the human kidney and the potential for applications within pathology, histology, and building anatomical atlases

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

Protocol for multimodal analysis of human kidney tissue by imaging mass spectrometry and CODEX multiplexed immunofluorescence.

Here, we describe the preservation and preparation of human kidney tissue for interrogation by histopathology, imaging mass spectrometry, and multiplexed immunofluorescence. Custom image registration and integration techniques are used to create cellular and molecular atlases of this organ system. Through careful optimization, we ensure high-quality and reproducible datasets suitable for cross-patient comparisons that are essential to understanding human health and disease. Moreover, each of these steps can be adapted to other organ systems or diseases, enabling additional atlas efforts

Highly multiplexed, label-free proteoform imaging of tissues by individual ion mass spectrometry.

Imaging of proteoforms in human tissues is hindered by low molecular specificity and limited proteome coverage. Here, we introduce proteoform imaging mass spectrometry (PiMS), which increases the size limit for proteoform detection and identification by fourfold compared to reported methods and reveals tissue localization of proteoforms at <80-μm spatial resolution. PiMS advances proteoform imaging by combining ambient nanospray desorption electrospray ionization with ion detection using individual ion mass spectrometry. We demonstrate highly multiplexed proteoform imaging of human kidney, annotating 169 of 400 proteoforms of <70 kDa using top-down MS and a database lookup of ~1000 kidney candidate proteoforms, including dozens of key enzymes in primary metabolism. PiMS images reveal distinct spatial localizations of proteoforms to both anatomical structures and cellular neighborhoods in the vasculature, medulla, and cortex regions of the human kidney. The benefits of PiMS are poised to increase proteome coverage for label-free protein imaging of tissues

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/