Single-cell proteomics in complex tissues using microprobe capillary electrophoresis mass spectrometry.

Pancreatic Cancer Ductal Adenocarcinoma (PDAC) is one of the deadliest cancers with 5-year survival of 11%. Understanding the intratumor heterogeneity is a pivotal piece to unravel the complexity of PDAC. While single cell RNASeq identifies the heterogeneous cell populations within the tumor tissue, spatially characterizing the transcriptomic profile of neoplastic and pre-neoplastic populations within the tissue remains a challenge, as the spatial dimension is usually lost upon tissue dissociation. We identified an approach to integrate spatial transcriptomics data with single cell RNASeq data. To characterize the cell populations within the tissue we performed single cell RNASeq on disease pathology-free pancreas tissue and primary PDAC samples. We profiled the transcriptomic profile of Acinar, Ductal, Acinar-to-Ductal (ADM), and Pancreatic Intraepithelial Neoplasia (PanINs) regions of interest (ROIs) across the tissue using the Nanostring GeoMx platform. Differential gene expression analysis using linear mixed-effect models of the cell-type specific ROIs defined pan-marker gene sets for each cell type, which were mapped to UMAP projections of single cell RNA sequencing data using AUCell scoring. As expected, the acinar pan-markers gene set derived from the spatial transcriptomics mapped to the manually annotated acinar population in the single cell data. On the other hand, Ductal, ADM, and PanIN pan-marker gene sets were mapped to distinct clusters that previously were not well-defined by single cell sequencing. The analysis coupled with orthogonal validation using RNAScope revealed gene signatures uniquely specific to ADM lesions and PanINs, respectively. Interestingly, our list included known markers as well as novel findings, supporting the validity of the findings. Furthermore, RNA velocity analysis using scVelo revealed a trajectory of cell evolution originating from acinar cells passing through the newly-defined ADM population and ending towards the ductal population derived from tumor samples. Overall, this integration approach of spatial and single cell transcriptomics can further define the characteristics that differentiate neoplastic and pre-neoplastic populations, as well as the potential drivers for tumorigenesis that could be therapeutically targeted. Citation Format: Ahmed M. Elhossiny, Eileen Carpenter, Padma Kadiyala, Yaqing Zhang, Filip Bednar, Arvind Rao, Timothy Frankel, Marina Pasca Di Magliano. Integrating single cell and spatial transcriptomics define gene signature for pancreatic ductal adenocarcinoma pre-neoplastic lesion [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr A006.

Single cell metabolomics is an emerging and rapidly developing field that complements developments in single cell analysis by genomics and proteomics. Major goals include mapping and quantifying the metabolome in sufficient detail to provide useful information about cellular function in highly heterogeneous systems such as tissue, ultimately with spatial resolution at the individual cell level. The chemical diversity and dynamic range of metabolites poses particular challenges for detection, identification and quantification. In this review we discuss both significant technical issues of measurement and interpretation, and progress toward addressing them, with recent examples from diverse biological systems. We provide a framework for further directions aimed at improving workflow and robustness so that such analyses may become commonly applied, especially in combination with metabolic imaging and single cell transcriptomics and proteomics.

Comparison of CE- and MPS-based analyses of forensic markers in a single cell after whole genome amplification

The quantitative analysis of protein mixtures is pivotal for the understanding of variations in the proteome of living systems. Therefore, approaches have been recently devised that generally allow the relative quantitative analysis of peptides and proteins. Here we present proof of concept of the new metal-coded affinity tag (MeCAT) technique, which allowed the quantitative determination of peptides and proteins. A macrocyclic metal chelate complex (1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA)) loaded with different lanthanides (metal(III) ions) was the essential part of the tag. The combination of DOTA with an affinity anchor for purification and a reactive group for reaction with amino acids constituted a reagent that allowed quantification of peptides and proteins in an absolute fashion. For the quantitative determination, the tagged peptides and proteins were analyzed using flow injection inductively coupled plasma MS, a technique that allowed detection of metals with high precision and low detection limits. The metal chelate complexes were attached to the cysteine residues, and the course of the labeling reaction was followed using SDS-PAGE and MALDI-TOF MS, ESI MS, and inductively coupled plasma MS. To limit the width in isotopic signal spread and to increase the sensitivity for ESI analysis, we used the monoisotopic lanthanide macrocycle complexes. Peptides tagged with the reagent loaded with different metals coelute in liquid chromatography. In first applications with proteins, the calculated detection limit for bovine serum albumin for example was 110 amol, and we have used MeCAT to analyze proteins of the Sus scrofa eye lens as a model system. These data showed that MeCAT allowed quantification not only of peptides but also of proteins in an absolute fashion at low concentrations and in complex mixtures.

Techniques that allow single cell analysis are gaining widespread attention, and most of these studies utilize genomics-based approaches. While nanofluidic technologies have enabled mass spectrometric analysis of single cells, these measurements have been limited to metabolomics and lipidomic studies. Single cell proteomics has the potential to improve our understanding of intercellular heterogeneity. However, this approach has faced challenges including limited sample availability, as well as a requirement of highly sensitive methods for sample collection, cleanup, and detection. We present a technique to overcome these limitations by combining a micropipette (pulled glass capillary) based sample collection strategy with offline sample preparation and nanoLC-MS/MS to analyze proteins through a bottom-up proteomic strategy. This study explores two types of proteomics data acquisition strategies namely data-dependent (DDA) and data-independent acquisition (DIA). Results from the study indicate DIA to be more sensitive enabling analysis of >1600 proteins from ∼130 μm Xenopus laevis embryonic cells containing <6 nL of cytoplasm. The method was found to be robust in obtaining reproducible protein quantifications from single cells spanning the 1-128-cell stages of development. Furthermore, we used micropipette sampling to study intercellular heterogeneity within cells in a single embryo and investigated embryonic asymmetry along both animal-vegetal and dorsal-ventral axes during early stages of development. Investigation of the animal-vegetal axis led to discovery of various asymmetrically distributed proteins along the animal-vegetal axis. We have further compared the hits found from our proteomic data sets with other studies and validated a few hits using an orthogonal imaging technique. This study forms the first report of vegetal enrichment of the germ plasm associated protein DDX4/VASA in Xenopus embyos. Overall, the method and data presented here holds promise to enable important leads in developmental biology.

A shotgun lipidomics approach that allowed the analysis of eight lipid classes directly from crude extracts of the soil bacterium Sinorhizobium meliloti is presented. New MS-MS transitions are reported for the analysis of monomethylphosphatidylethanolamines, dimethylphosphatidylethanolamines, and three bacterial non-phosphorus-containing lipid classes [sulfoquinovosyldiacylglycerols, ornithines, and diacylglyceryl-(N,N,N-trimethyl)-homoserines]. Unique MS-MS transitions allowed the analysis of isomeric species from various lipid classes without chromatography. Analyses required small sample amounts and minimal preparation; thus, this methodology has excellent potential to be used as a screening tool for the analysis of large numbers of samples in functional genomics studies. FA distributions within lipid classes of S. meliloti are described for the first time, and the relative positions of fatty acyl substituents (sn-1, sn-2) in phospholipids are presented. FA distributions in diacylglyceryl-(N,N,N-trimethyl)-homoserines were identical to those of phospholipids, indicating a common biosynthetic origin for these lipids. The method was applied to the analysis of mutants deficient in the PhoB regulator protein. Increased lipid cyclopropanation was observed in PhoB-deficient mutants under P(i) starvation.

Single-cell proteomics can provide unique insights into biological processes by resolving heterogeneity that is obscured by bulk measurements. Gains in the overall sensitivity and proteome coverage through improvements in sample processing and analysis increase the information content obtained from each cell, particularly for less abundant proteins. Here we report on improved single-cell proteome coverage through the combination of the previously developednanoPOTS platform with further miniaturization of liquid chromatography (LC) separations and implementation of an ultrasensitive latest generation mass spectrometer. Following nanoPOTS sample preparation, protein digests from single cells were separated using a 20 μm i.d. in-house-packed nanoLC column. Separated peptides were ionized using an etched fused-silica emitter capable of stable operation at the ∼20 nL/min flow rate provided by the LC separation. Ultrasensitive LC-MS analysis was achieved using the Orbitrap Eclipse Tribrid mass spectrometer. An average of 362 protein groups were identified by tandem mass spectrometry (MS/MS) from single HeLa cells, and 874 protein groups were identified using the Match Between Runs feature of MaxQuant. This represents an >70% increase in label-free proteome coverage for single cells relative to previous efforts using larger bore (30 μm i.d.) LC columns coupled to a previous-generation Orbitrap Fusion Lumos mass spectrometer.

Clinical proteomics involves the analysis of protein expression of disease proteomes, with the aim of solving a specific clinical problem. Discoveries made from proteomicbased studies contribute to the growing need for innovative medical diagnostics for disease detection. Taking into consideration the global health burden of cardiac disease, clinical proteomics is a valuable tool to improve risk stratification associated with this disease. In cardiovascular medicine, the identification of novel proteins, or biomarkers, that are differentially expressed in cardiac disease proteomes may enable early detection of the disease state, thereby preventing progression to disease end points. This review outlines various proteomic platforms and their technical advancements and relates these to the cardiovascular sciences. The entire protein complement of the cell, or proteome, is dynamic and changes in response to the disease state. 1 Proteomic-based experiments can be used to characterize such alterations in protein expression during disease progression. 2 With combined improvements in mass spectrometry (MS) technology as well as innovative molecular biology screening tools, there has been widespread growth in the characterization of cardiac disease proteomes. In fact, proteomic technology has been an important tool in the analysis of heart failure (HF), 3,4 cardiac hypertrophy, 5,6 and dilated cardiomyopathy.7 Several of the overarching aims of such studies include providing greater understanding of general biological mechanisms as well as identifying unique proteins that are clinically useful in the detection of cardiac disease in the early stages or potentially used as novel therapeutic targets. Clinical proteomics continues to benefit from advancements in technologies that allow for fast and consistent identification of proteins with corresponding increases in the dynamic range of proteins detectable in the disease proteome. MS-related technologies have improved in their ability to detect low-abundance proteins as well as membrane proteins and have benefited from sample preprocessing strategies that decrease the complexity of large-scale analyses. With the emergence of different methodologies for biomarker identification, the following 3 criteria must be taken into careful consideration to determine whether the protein is, in fact,

Single cell genomics and proteomics with the combination of innovative three-dimensional (3D) cell culture techniques can open new avenues toward the understanding of intra-tumor heterogeneity. Here, we characterize lung cancer markers using single cell mass cytometry to compare different in vitro cell culturing methods: two-dimensional (2D), carrier-free, or bead-based 3D culturing with in vivo xenografts. Proliferation, viability, and cell cycle phase distribution has been investigated. Gene expression analysis enabled the selection of markers that were overexpressed: TMEM45A, SLC16A3, CD66, SLC2A1, CA9, CD24, or repressed: EGFR either in vivo or in long-term 3D cultures. Additionally, TRA-1-60, pan-keratins, CD326, Galectin-3, and CD274, markers with known clinical significance have been investigated at single cell resolution. The described twelve markers convincingly highlighted a unique pattern reflecting intra-tumor heterogeneity of 3D samples and in vivo A549 lung cancer cells. In 3D systems CA9, CD24, and EGFR showed higher expression than in vivo. Multidimensional single cell proteome profiling revealed that 3D cultures represent a transition from 2D to in vivo conditions by intermediate marker expression of TRA-1-60, TMEM45A, pan-keratin, CD326, MCT4, Gal-3, CD66, GLUT1, and CD274. Therefore, 3D cultures of NSCLC cells bearing more putative cancer targets should be used in drug screening as the preferred technique rather than the Petri-dish.

Depending on age, phase in the cell cycle, nutrition, and environmental factors, individual cells exhibit large metabolic diversity. To explore metabolic variations in cell populations, laser ablation electrospray ionization (LAESI) mass spectrometry (MS) was used for the in situ analysis of individual cells at atmospheric pressure. Single cell ablation was achieved by delivering mid-IR laser pulses through the etched tip of a GeO(2)-based glass fiber. Metabolic analysis was performed from single cells and small cell populations of Allium cepa and Narcissus pseudonarcissus bulb epidermis, as well as single eggs of Lytechinus pictus. Of the 332 peaks detected for A. cepa, 35 were assigned to metabolites with the help of accurate ion masses and tandem MS. The metabolic profiles from single cells of the two plant species included a large variety of oligosaccharides including possibly fructans in A. cepa, and alkaloids, e.g., lycorine in N. pseudonarcissus. Analysis of adjacent individual cells with a difference in pigmentation showed that, in addition to essential metabolites found in both variants, the pigmented cells contained anthocyanidins, other flavonoids, and their glucosides. Analysis of single epidermal cells from different scale leaves in an A. cepa bulb showed metabolic differences corresponding to their age. Our results indicate the feasibility of using LAESI-MS for the in situ analysis of metabolites in single cells with potential applications in studying cell differentiation, changes due to disease states, and response to xenobiotics.

Clonal genetic diversity is a hallmark of tumor biology. In many cancers, clonal evolution has been implicated as a driver of tumor initiation, progression, and relapse, and can be a prognostic indicator of disease outcome. Cancer cells are also phenotypically heterogeneous, constituting features which can enable intravasation and metastasis, or which can impart immunoevasive capabilities. Clinically, a combination of single-cell genotype and phenotype data from cancer biopsies could provide valuable insights into oncological disease states, such as metastatic risk or drug response. Here we present a multiomic technology for the simultaneous, high-throughput capture of DNA genotype and protein information from single cells, demonstrated through the analysis of acute myeloid leukemia (AML) derived cell lines. The experimental workflow leverages the high-throughput single-cell microfluidic droplet technology on the Mission Bio Tapestri platform, a system optimized to conduct multiplex targeted genomic DNA amplification from &amp;gt;50 AML-associated loci. In our modified workflow, cells are stained with a pool of monoclonal antibodies conjugated with barcoded oligonucleotides, enabling a sequencing-based readout of single-cell protein signatures. The sequencing data is analyzed with a bioinformatic pipeline that separates antibody signal from targeted genotyping data on a cell-by-cell basis. We find that on a mixed population of myelogenous leukemia cells and B-cells, single-cell genotype information effectively distinguishes between cell lines, while the protein data independently recapitulates the biological signal from conventional flow cytometric methods. In conclusion, this technology opens up a new era for discovery using multiomic analysis of the complex relationships between genotype and phenotype in cancer, paving the way for future studies analyzing primary tissue samples. Citation Format: Benjamin Demaree, Cyrille Delley, David Ruff, Sebastian Treusch, Dennis Eastburn, Keith Jones, Adam Abate. Combined high-throughput DNA genotyping and protein quantification in single cancer cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3527.

Bisphenol E (BPE) and bisphenol S (BPS) have recently replaced bisphenol A as monomers for producing polycarbonates. However, BPE and BPS can pose hazards as they are known to be endocrine disruptors. Despite the huge increase in their use, there is a lack of data regarding the toxicity and effects of BPE and BPS. We investigated the photoinduced transformation of BPE and BPS when subjected to sun-simulated radiation and using TiO2 as a photocatalyst. Analyses of BPE, BPS and their by-products were performed by high-performance liquid chromatography/high-resolution mass spectrometry (HPLC/HRMS) using an orbitrap mass analyzer in negative electrospray ionisation (ESI) mode. The chromatographic separations were achieved by employing a C18 reversed-phase column, and the transformation products (TPs) were elucidated structurally using HRMS and multistage MS experiments performed in collision-induced dissociation (CID) mode. The transformation of bisphenol S involved the formation of twelve by-products, while ten TPs were detected following BPE degradation. For bisphenol S, the cleavage of the molecule is a very important transformation route, together with the hydroxylation of the substrate to provide mono- and poly-hydroxylated TPs. For bisphenol E, the two main routes were hydroxylation and ring opening. Acute toxicity for BPS, BPE and their TPs was assessed using the Vibrio fischeri assay, highlighting that their initial transformation involved the formation of TPs that were more toxic than the parent compound. The HPLC/HRMS method developed was useful for characterising and identifying newly formed TPs from bisphenol E and bisphenol S. This study aimed to examine the structure of twenty by-products identified during TiO2 -mediated photolysis and to evaluate acute toxicity over time.

Global quantification of protein abundances in single cells could provide direct information on cellular phenotypes and complement transcriptomics measurements. However, single-cell proteomics is still immature and confronts many technical challenges. Herein we describe a nested nanoPOTS (N2) chip to improve protein recovery, operation robustness, and processing throughput for isobaric-labeling-based scProteomics workflow. The N2 chip reduces reaction volume to <30 nL and increases capacity to >240 single cells on a single microchip. The tandem mass tag (TMT) pooling step is simplified by adding a microliter droplet on the nested nanowells to combine labeled single-cell samples. In the analysis of ~100 individual cells from three different cell lines, we demonstrate that the N2 chip-based scProteomics platform can robustly quantify ~1500 proteins and reveal membrane protein markers. Our analyses also reveal low protein abundance variations, suggesting the single-cell proteome profiles are highly stable for the cells cultured under identical conditions.

Effective extension of mass spectrometry-based proteomics to single cells remains challenging. Herein we combined microfluidic nanodroplet technology with tandem mass tag (TMT) isobaric labeling to significantly improve analysis throughput and proteome coverage for single mammalian cells. Isobaric labeling facilitated multiplex analysis of single cell-sized protein quantities to a depth of ∼1 600 proteins with a median CV of 10.9% and correlation coefficient of 0.98. To demonstrate in-depth high throughput single cell analysis, the platform was applied to measure protein expression in 72 single cells from three murine cell populations (epithelial, immune, and endothelial cells) in <2 days instrument time with over 2 300 proteins identified. Principal component analysis grouped the single cells into three distinct populations based on protein expression with each population characterized by well-known cell-type specific markers. Our platform enables high throughput and unbiased characterization of single cell heterogeneity at the proteome level.

Development and Application of High-Throughput Single Cell Lipid Profiling: A Study of &lt;i&gt;SNCA-A53T&lt;/i&gt; Human Dopamine Neurons