Exploring the Fundamental Structures of Life: Non-Targeted, Chemical Analysis of Single Cells and Subcellular Structures

Single-Cell Classification Using Mass Spectrometry through Interpretable Machine Learning

The brain consists of organized ensembles of cells that exhibit distinct morphologies, cellular connectivity, and dynamic biochemistries that control the executive functions of an organism. However, the relationships between chemical heterogeneity, cell function, and phenotype are not always understood. Recent advancements in matrix-assisted laser desorption/ionization mass spectrometry have enabled the high-throughput, multiplexed chemical analysis of single cells, capable of resolving hundreds of molecules in each mass spectrum. We developed a machine learning workflow to classify single cells according to their mass spectra based on cell groups of interest (GOI), e.g., neurons vs astrocytes. Three data sets from various cell groups were acquired on three different mass spectrometer platforms representing thousands of individual cell spectra that were collected and used to validate the single cell classification workflow. The trained models achieved >80% classification accuracy and were subjected to the recently developed instance-based model interpretation framework, SHapley Additive exPlanations (SHAP), which locally assigns feature importance for each single-cell spectrum. SHAP values were used for both local and global interpretations of our data sets, preserving the chemical heterogeneity uncovered by the single-cell analysis while offering the ability to perform supervised analysis. The top contributing mass features to each of the GOI were ranked and selected using mean absolute SHAP values, highlighting the features that are specific to the defined GOI. Our approach provides insight into discriminating the chemical profiles of the single cells through interpretable machine learning, facilitating downstream analysis and validation.

The up-to-date strategies for the isolation and manipulation of single cells

Due to the large cellular heterogeneity, the strategies for the isolation and manipulation of single cells have been pronounced indispensable in the fields of disease diagnostics, drug delivery, and cancer biology at the single-cell resolution. Herein, an overview of the up-to-date techniques for precise manipulation/separation and analysis of single-cell is accomplished, these include the various approaches for the isolation and detection of individual cells in flow cytometry, microfluidic systems, micromodule systems, and others. In addition, the advanced application of these protocols is discussed. In particular, a few designs are highlighted for visualization, non-invasion, and intelligentization in single cell analysis, i.e., imaging flow cytometry, label-free microfluidic platform, single-cell capillary probe, and other related techniques. At the present, the main barriers in the various schemes for single cell manipulation which limited their practical applications are their cumbersome construction and single-functionality. The future opportunities and outstanding challenges in the isolation/manipulation of single cells are depicted.

Preserving Single Cells in Space and Time for Analytical Assays

Analytical assays performed within clinical laboratories influence roughly 70% of all medical decisions by facilitating disease detection, diagnosis, and management. Both in clinical and academic research laboratories, single-cell assays permit measurement of cell diversity and identification of rare cells, both of which are important in the understanding of disease pathogenesis. For clinically utility, the single-cell assays must be compatible with the clinical workflow steps of sample collection, sample transportation, pre-analysis processing, and single-cell assay; therefore, it is paramount to preserve cells in a state that resembles that in vivo rather than measuring signaling behaviors initiated in response to stressors such as sample collection and processing. To address these challenges, novel cell fixation (and more broadly, cell preservation) techniques incorporate programmable fixation times, reversible bond formation and cleavage, chemoselective reactions, and improved analyte recovery. These technologies will further the development of individualized, precision therapies for patients to yield improved clinical outcomes.

High-Performance Molecular Imaging with MALDI Trapped Ion-Mobility Time-of-Flight (timsTOF) Mass Spectrometry

Imaging mass spectrometry (IMS) enables the spatially targeted molecular assessment of biological tissues at cellular resolutions. New developments and technologies are essential for uncovering the molecular drivers of native physiological function and disease. Instrumentation must maximize spatial resolution, throughput, sensitivity, and specificity, because tissue imaging experiments consist of thousands to millions of pixels. Here, we report the development and application of a matrix-assisted laser desorption/ionization (MALDI) trapped ion-mobility spectrometry (TIMS) imaging platform. This prototype MALDI timsTOF instrument is capable of 10 μm spatial resolutions and 20 pixels/s throughput molecular imaging. The MALDI source utilizes a Bruker SmartBeam 3-D laser system that can generate a square burn pattern of <10 × 10 μm at the sample surface. General image performance was assessed using murine kidney and brain tissues and demonstrate that high-spatial-resolution imaging data can be generated rapidly with mass measurement errors <5 ppm and ∼40 000 resolving power. Initial TIMS-based imaging experiments were performed on whole-body mouse pup tissue demonstrating the separation of closely isobaric [PC(32:0) + Na]+ and [PC(34:3) + H]+ (3 mDa mass difference) in the gas phase. We have shown that the MALDI timsTOF platform can maintain reasonable data acquisition rates (>2 pixels/s) while providing the specificity necessary to differentiate components in complex mixtures of lipid adducts. The combination of high-spatial-resolution and throughput imaging capabilities with high-performance TIMS separations provides a uniquely tunable platform to address many challenges associated with advanced molecular imaging applications.

Single-cell identification by microfluidic-based in situ extracting and online mass spectrometric analysis of phospholipids expression

This work describes a microfluidic system for in situ extraction of a single-cell and its phosphatidylcholine analysis through mass spectrometry. This approach uncovered cellular heterogeneity among seemingly identical cells and provided a new platform for identification and classification of cells.

Laser cleavable probes for in situ multiplexed glycan detection by single cell mass spectrometry

A single-cell MS approach for multiplexed glycan detection to investigate the relationship between drug resistance and glycans at a single-cell level and quantify multiple glycans, overcoming the limit of low ionization efficiency of glycans.

Glycans binding on the cell surface through glycosylation play a key role in controlling various cellular processes, and glycan analysis at a single-cell level is necessary to study cellular heterogeneity and diagnose diseases in the early stage. Herein, we synthesized a series of laser cleavable probes, which could sensitively detect glycans on single cells and tissues by laser desorption ionization mass spectrometry (LDI-MS). This multiplexed and quantitative glycan detection was applied to evaluate the alterations of four types of glycans on breast cancer cells and drug-resistant cancer cells at a single-cell level, indicating that drug resistance may be related to the upregulation of glycan with a β-d-galactoside (Galβ) group and Neu5Aca2-6Gal(NAc)-R. Moreover, the glycan spatial distribution in cancerous and paracancerous human tissues was also demonstrated by MS imaging, showing that glycans are overexpressed in cancerous tissues. Therefore, this single-cell MS approach exhibits promise for application in studying glycan functions which are essential for clinical biomarker discovery and diagnosis of related diseases.

Lipid Analysis of 30 000 Individual Rodent Cerebellar Cells Using High-Resolution Mass Spectrometry

Single-cell measurements aid our understanding of chemically heterogeneous systems such as the brain. Lipids are one of the least studied chemical classes, and their cell-to-cell heterogeneity remains largely unexplored. We adapted microscopy-guided single-cell profiling using matrix-assisted laser desorption/ionization ion cyclotron resonance mass spectrometry to profile the lipid composition of over 30 000 individual rat cerebellar cells. We detected 520 lipid features, many of which were found in subsets of cells; Louvain clustering identified 101 distinct groups that can be correlated to neuronal and astrocytic classifications and lipid classes. Overall, the two most common lipids found were [PC(32:0)+H]⁺ and [PC(34:1)+H]⁺, which were present within 98.9 and 89.5% of cells, respectively; lipid signals present in <1% of cells were also detected, including [PC(34:1)+K]⁺ and [PG(40:2(OH))+Na]⁺. These results illustrate the vast lipid heterogeneity found within rodent cerebellar cells and hint at the distinct functional consequences of this heterogeneity.