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MALDI imaging allows for the near-cellular profiling of proteoforms directly from microbial colonies, or plant and mammalian tissues. Despite detecting hundreds of proteoforms, identification of unknowns with only intact mass information remains a distinct challenge, even at with high resolution and mass accuracy. To this end many supplementary methods have been used to create experimental databases for accurate mass matching, including bulk or spatially resolved bottom-up and/or top-down proteomics. Herein we describe the application of 193 nm ultraviolet photodissociation (UVPD) for fragmentation of quadrupole isolated singly charged ubiquitin (m/z 8565) by MALDI-UVPD on an UHMR HF Orbitrap. This platform permitted the high-resolution accurate mass measurement of not just terminal fragments, but also large internal fragments. The outlined workflow demonstrated the feasibility of high resolution top-down analysis of MALDI generated protein ions and the potential towards more comprehensive characterization of proteoforms in MALDI imaging applications.

Core histones including H2A, H2B, H3, and H4 are key modulators of cellular repair, transcription, and replication within eukaryotic cells, playing vital roles in the pathogenesis of disease and cellular responses to environmental stimuli. Traditional mass spectrometry (MS)-based bottom-up and top-down proteomics allows for the comprehensive identification of proteins and of post-translational modification (PTM) harboring proteoforms. However, these methodologies have difficulties preserving near-cellular spatial distributions because they typically require laser capture microdissection (LCM) and advanced sample preparation techniques. Herein, we coupled a matrix-assisted laser desorption/ionization (MALDI) source with a Thermo Scientific Q Exactive HF Orbitrap MS upgraded with ultrahigh mass range (UHMR) boards for the first demonstration of complementary high-resolution accurate mass (HR/AM) measurements of proteoforms up to 16.5 kDa directly from tissues using this benchtop mass spectrometer. The platform achieved isotopic resolution throughout the detected mass range, providing confident assignments of proteoforms with low ppm mass error and a considerable increase in duty cycle over other Fourier transform mass analyzers. Proteoform mapping of core histones was demonstrated on sections of human kidney at near-cellular spatial resolution, with several key distributions of histone and other proteoforms noted within both healthy biopsy and a section from a renal cell carcinoma (RCC) containing nephrectomy. The use of MALDI-MS imaging (MSI) for proteoform mapping demonstrates several steps toward high-throughput accurate identification of proteoforms and provides a new tool for mapping biomolecule distributions throughout tissue sections in extended mass ranges.

International audience; Generating top-down tandem mass spectra (MS/MS) from complex mixtures of proteoforms benefits from improvements in fractionation, separation, fragmentation, and mass analysis. The algorithms to match MS/MS to sequences have undergone a parallel evolution, with both spectral alignment and match-counting approaches producing high-quality proteoform-spectrum matches (PrSMs). This study assesses state-of-the-art algorithms for top-down identification (ProSight PD, TopPIC, MSPathFinderT, and pTop) in their yield of PrSMs while controlling false discovery rate. We evaluated deconvolution engines (ThermoFisher Xtract, Bruker AutoMSn, Matrix Science Mascot Distiller, TopFD, and FLASHDeconv) in both ThermoFisher Orbitrap-class and Bruker maXis Q-TOF data (PXD033208) to produce consistent precursor charges and mass determinations. Finally, we sought post-translational modifications (PTMs) in proteoforms from bovine milk (PXD031744) and human ovarian tissue. Contemporary identification workflows produce excellent PrSM yields, although approximately half of all identified proteoforms from these four pipelines were specific to only one workflow. Deconvolution algorithms disagree on precursor masses and charges, contributing to identification variability. Detection of PTMs is inconsistent among algorithms. In bovine milk, 18% of PrSMs produced by pTop and TopMG were singly phosphorylated, but this percentage fell to 1% for one algorithm. Applying multiple search engines produces more comprehensive assessments of experiments. Top-down algorithms would benefit from greater interoperability.

High throughput native mass spectrometry analysis of proteins and protein complexes has been enabled by recent development of infusion and liquid chromatography (LC) systems, which often include complete LC pumps without fully utilizing their gra-dient flows. We demonstrated a lower-cost infusion cart for native mass spectrometry applications using a single isocratic solvent pump that can operate at both nano- and high-flow configurations (0.05-150 µL/min) for both infusion and online buffer exchange experiments. The platform is controlled via open-source software and can potentially be expanded for customized experimental designs, offering a lower cost alternative to labs with limited budget and/or needs in student training.

Due to its speed, accuracy, and adaptability to various sample types, matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) has become a popular method to identify molecular isotope profiles from biological samples. Often MALDI-MS data does not include tandem MS fragmentation data, and thus the identification of compounds in samples requires external databases so that the accurate mass of detected signals can be matched to known molecular compounds. Most relevant software tools are focused on small molecules (e.g., metabolites, lipids), and cannot be easily adapted to protein data due to their more complex isotopic distributions. Here, we present an R package called IsoMatchMS for the automated annotation of MALDI-MS data for multiple datatypes (e.g., intact proteins, lipids, metabolites, etc.). This tool accepts already computed molecular formulas, or for proteomics applications, can compute molecular formulas from a list of input peptides or proteins including proteins with post-translational modifications. Visualization of all matched isotopic profiles are provided in a highly accessible HTML format called a trelliscope display, which allows users to filter and sort by several parameters such as match scores and the number of peaks matched. IsoMatchMS simplifies the annotation and visualization of MALDI-MS data for downstream analyses.

SARS-CoV-2 cellular infection is mediated by the heavily glycosylated spike protein. Recombinant versions of the spike protein and the receptor-binding domain (RBD) are necessary for seropositivity assays and can potentially serve as vaccines against viral infection. RBD plays key roles in the spike protein's structure and function, and thus, comprehensive characterization of recombinant RBD is critically important for biopharmaceutical applications. Liquid chromatography coupled to mass spectrometry has been widely used to characterize post-translational modifications in proteins, including glycosylation. Most studies of RBDs were performed at the proteolytic peptide (bottom-up proteomics) or released glycan level because of the technical challenges in resolving highly heterogeneous glycans at the intact protein level. Herein, we evaluated several online separation techniques: (1) C2 reverse-phase liquid chromatography (RPLC), (2) capillary zone electrophoresis (CZE), and (3) acrylamide-based monolithic hydrophilic interaction chromatography (HILIC) to separate intact recombinant RBDs with varying combinations of glycosylations (glycoforms) for top-down mass spectrometry (MS). Within the conditions we explored, the HILIC method was superior to RPLC and CZE at separating RBD glycoforms, which differ significantly in neutral glycan groups. In addition, our top-down analysis readily captured unexpected modifications (e.g., cysteinylation and N-terminal sequence variation) and low abundance, heavily glycosylated proteoforms that may be missed by using glycopeptide data alone. The HILIC top-down MS platform holds great potential in resolving heterogeneous glycoproteins for facile comparison of biosimilars in quality control applications.

Despite the successful application of tandem mass tags (TMT) for peptide quantitation, missing reporter ions in higher energy collisional dissociation (HCD) spectra remains a challenge for consistent quantitation, especially for peptides with labile post-translational modifications. Ultraviolet photodissociation (UVPD) is an alternative ion activation method shown to provide superior coverage for sequencing of peptides and intact proteins. Here, we optimized and evaluated 193 nm UVPD for the characterization of TMT-labeled model peptides, HeLa proteome, and N-glycopeptides from model proteins. UVPD yielded the same TMT reporter ions as HCD, at m/z 126–131. Additionally, UVPD produced a wide range of fragments that yielded more complete characterization of glycopeptides and less frequent missing TMT reporter ion channels, whereas HCD yielded a strong tradeoff between characterization and quantitation of TMT-labeled glycopeptides. However, the lower fragmentation efficiency of UVPD yielded fewer peptide identifications than HCD. Overall, 193 nm UVPD is a valuable tool that provides an alternative to HCD for the quantitation of large and highly modified peptides with labile PTMs. Continued development of instrumentation specific to UVPD will yield greater fragmentation efficiency and fulfil the potential of UVPD to be an all-in-one spectrum ion activation method for broad use in the field of proteomics.

Generating top-down tandem mass spectra (MS/MS) for complex mixtures of proteoforms has become possible through improvements in fractionation, on-line separation, dissociation, and mass analysis. The algorithms to match MS/MS to sequences have undergone a parallel evolution, with both spectral alignment and peak matching being paired with diverse methods for scoring proteoform-spectral matches (PrSMs). This study assesses state-of-the-art algorithms for top-down identification through three distinct challenges. The first is identifying a large yield of PrSMs while controlling false discovery rate (FDR) in identifying thousands of proteoforms from complex cell lysates via four software workflows: ProSight Proteome Discoverer, TopPIC, Informed Proteomics, and pTop. The second is the deconvolution of data from both Thermo Orbitrap-class and Bruker maXis Q-TOF instruments to produce consistent precursor charge and mass determinations while generating fragment mass lists to optimize identification. The third attempts to detect diverse post-translational modifications (PTMs) in proteoforms from bovine milk and human ovarian tissue. The data demonstrate that existing software suites produce admirable sensitivity, in some cases identifying a third of collected MS/MS with FDR controlled below 2%; the overlap in these PrSMs, however, illustrates real value in searching data with multiple search engines. Differences among identification workflows seem to result from each search algorithm incorporating its own deconvolution algorithm. By transmitting deconvolution data from multiple deconvolution routes (Thermo Xtract, Bruker Auto MSn, Mascot Distiller, TopFD, and FLASHDeconv) to the downstream TopPIC search algorithm, we were able to detect common causes of deconvolution disagreement. The detection of PTMs was very inconsistent among search algorithms, with some workflows suggesting as little as 1% of PrSMs from bovine milk were singly-phosphorylated while other workflows found that 18% of PrSMs were singly-phosphorylated. Taken together, these results make a strong argument for top-down researchers to adopt a standard practice of analyzing each MS/MS experiment with at least two different search engines.

Amelogenin constitutes ~ 90% of the enamel matrix in the secretory stage of amelogenesis, a still poorly understood process that results in the formation of the hardest and most mineralized tissue in vertebrates - enamel. Most biophysical research with amelogenin uses recombinant protein expressed in Escherichia coli. In addition to providing copious amounts of protein, recombinant expression allows

Proteomic methods have been widely used to study proteins in complex biological samples to understand biological molecular mechanisms. Most well-established methods (known as bottom-up proteomics, BUP) employ an enzymatic digestion step to cleave intact proteins into smaller peptides for liquid chromatography (LC) mass spectrometry (MS) detection. In contrast, top-down proteomics (TDP) directly characterizes intact proteins including all possible post-translational modifications (PTMs), thus offering unique insights into proteoform biology where combinations of individual PTMs may play important roles. We performed TDP on soybean root nodules infected by the symbiotic Bradyrhizobium japonicum in both the wildtype bacterium and a nifH- mutant, which lacks the ability to fix nitrogen in the soybean root nodule. TDP captured 1648 proteoforms derived from 313 bacterial genes and 178 soybean genes. Leghemoglobin, the most abundant protein in the sample, existed in many truncated proteoforms. Interestingly, these truncated proteoforms were considerably more abundant in the wildtype relative to the nifH- mutant, implicating protease activity as an important factor in nitrogen fixation. Proteoforms with various PTMs and combinations thereof were identified using an unrestricted open modification search. This included less common PTMs such as myristoylation, palmitoylation, cyanylation, and sulfation. In parallel, we collected high resolution MS imaging (MSI) data of intact proteins and biopolymers (

The protocol describes how to use laser capture microdissection (LCM) to cut small regions of interest (~200-300 μm) from tissue sections. This is followed by top down proteomics analysis by liquid chromatography - mass spectrometry (LC-MS).

Scope: An overall protocol outlining several nested sub-protocols for the 2D mapping of intact proteoforms from human tissue by MALDI imaging on an UHMR Q Exactive Orbitrap MS. Expected Outcomes: Annotated ion images of intact proteoforms ready to be ingested and disseminated.

This is the overall workflow for LCMS top down proteomics of pancreas functional units from tissue sections using the MicroPOTS platform. The expected outcomes are proteoform identification and quantitation values from selected tissue functional units.

This is the overall workflow for LCMS top down proteomics of kidney functional units from tissue sections using the MicroPOTS platform. The expected outcomes are proteoform identification and quantitation values from selected tissue functional units.

Scope: The protocol describes a workflow for processing mass spectrometry imaging (MSI) data of high resolution intact proteins/proteoforms by MALDI. It involves the visualization and image generation using commercial software and also peak annotation using open source code. Expected Outcomes: Tabular output of matched proteoforms, as well as a trelliscope display of overlaid isotopic distributions allowing for the annotation of high-resolution accurate mass distributions of proteoforms.

This protocol describe a workflow for top-down proteomics analysis. Top-down proteomics data are processed with two separate software packages TopPIC and TDPortal. Proteoform identifications were merged from the two software with unified FDR to increase coverage. TopPICR was separately used to cluster TopPIC proteoform to extract abundances for label-free quantitation.

We present an updated analysis of the linker and core histone proteins and their proteoforms in the green microalga Chlamydomonas reinhardtii by top-down mass spectrometry (TDMS). The combination of high-resolution liquid chromatographic separation, robust fragmentation, high mass spectral resolution, the application of a custom search algorithm, and extensive manual analysis enabled the characterization of 86 proteoforms across all four core histones H2A, H2B, H3, and H4 and the linker histone H1. All canonical H2A paralogs, which vary in their C-termini, were identified, along with the previously unreported noncanonical variant H2A.Z that had high levels of acetylation and C-terminal truncations. Similarly, a majority of the canonical H2B paralogs were identified, along with a smaller noncanonical variant, H2B.v1, that was highly acetylated. Histone H4 exhibited a novel acetylation profile that differs significantly from that found in other organisms. A majority of H3 was monomethylated at K4 with low levels of co-occuring acetylation, while a small fraction of H3 was trimethylated at K4 with high levels of co-occuring acetylation.

Trichoderma reesei is a "workhorse" fungus that produces glycosyl hydrolases (e.g., cellulases) at high titers for use in industrial bioprocessing. In this study, we focused on α-L-arabinofuranosidase, an enzyme important for the treatment of lignocellulosic biomass, but susceptible to oxidative damage that can occur during industrial processing. The molecular details that render this enzyme inactive have not yet been identified. To approach this issue, we used proteomics to identify amino acid residues that were oxidized after a relevant oxidative treatment (Fenton reaction). These oxidative modifications were included in the 3D protein structures, and using molecular dynamics simulations, we then studied the behaviors of non-modified and oxidized enzymes. These simulations showed significant alterations of the conformational stability of the protein when oxidized, as evidenced by changes in root mean square deviation (RMSD) and principal component analyses (PCA) trajectories. Likewise, enzyme-ligand interactions such as hydrogen bonds were greatly reduced in quantity and quality in the oxidized protein. Finally, free energy landscape plots showed that there was a more rugged energy surface in the oxidized protein, implying a less favorable reaction pathway. These results reveal the basis for loss of function in this carbohydrate active enzyme (CAZY) in the commercially relevant fungus T. reesei.

Proteomic investigations of Alzheimer’s and Parkinson’s disease have provided valuable insights into neurodegenerative disorders. Thus far, these investigations have largely been restricted to bottom-up approaches, hindering the degree to which one can characterize a protein’s “intact” state. Top-down proteomics (TDP) overcomes this limitation; however, it is typically limited to observing only the most abundant proteoforms and of a relatively small size. Therefore, fractionation techniques are commonly used to reduce sample complexity. Here, we investigate gas-phase fractionation through high-field asymmetric waveform ion mobility spectrometry (FAIMS) within TDP. Utilizing a high complexity sample derived from Alzheimer’s disease (AD) brain tissue, we describe how the addition of FAIMS to TDP can robustly improve the depth of proteome coverage. For example, implementation of FAIMS with external compensation voltage (CV) stepping at −50, −40, and −30 CV could more than double the mean number of non-redundant proteoforms, genes, and proteome sequence coverage compared to without FAIMS. We also found that FAIMS can influence the transmission of proteoforms and their charge envelopes based on their size. Importantly, FAIMS enabled the identification of intact amyloid beta (Aβ) proteoforms, including the aggregation-prone Aβ(1–42) variant which is strongly linked to AD. Raw data and associated files have been deposited to the ProteomeXchange Consortium via the MassIVE data repository with data set identifier PXD023607.

Visual examination of mass spectrometry data is necessary to assess data quality and to facilitate data exploration. Graphics provide the means to evaluate spectral properties, test alternative peptide/protein sequence matches, prepare annotated spectra for publication, and fine-tune parameters during wet lab procedures. Visual inspection of LC-MS data is constrained by proteomics visualization software designed for particular workflows or vendor-specific tools without open-source code. We built PSpecteR, an open-source and interactive R Shiny web application for visualization of LC-MS data, with support for several steps of proteomics data processing, including reading various mass spectrometry files, running open-source database search engines, labeling spectra with fragmentation patterns, testing post-translational modifications, plotting where identified fragments map to reference sequences, and visualizing algorithmic output and metadata. All figures, tables, and spectra are exportable within one easy-to-use graphical user interface. Our current software provides a flexible and modern R framework to support fast implementation of additional features. The open-source code is readily available (https://github.com/EMSL-Computing/PSpecteR), and a PSpecteR Docker container (https://hub.docker.com/r/emslcomputing/pspecter) is available for easy local installation.

The Betacoronavirus SARS-CoV-2 non-structural protein Nsp9 is a 113-residue protein that is essential for viral replication, and consequently, a potential target for the development of therapeutics against COVID19 infections. To capture insights into the dynamics of the protein’s backbone in solution and accelerate the identification and mapping of ligand-binding surfaces through chemical shift perturbation studies, the backbone 1H, 13C, and 15N NMR chemical shifts for Nsp9 have been extensively assigned. These assignments were assisted by the preparation of an ~ 70% deuterated sample and residue-specific, 15N-labelled samples (V, L, M, F, and K). A major feature of the assignments was the “missing” amide resonances for N96-L106 in the 1H-15N HSQC spectrum, a region that comprises almost the complete C-terminal α-helix that forms a major part of the homodimer interface in the crystal structure of SARS-CoV-2 Nsp9, suggesting this region either undergoes intermediate motion in the ms to μs timescale and/or is heterogenous. These “missing” amide resonances do not unambiguously appear in the 1H-15N HSQC spectrum of SARS-CoV-2 Nsp9 collected at a concentration of 0.0007 mM. At this concentration, at the detection limit, native mass spectrometry indicates the protein is exclusively in the monomeric state, suggesting the intermediate motion in the C-terminal of Nsp9 may be due to intramolecular dynamics. Perhaps this intermediate ms to μs timescale dynamics is the physical basis for a previously suggested “fluidity” of the C-terminal helix that may be responsible for homophilic (Nsp9-Nsp9) and postulated heterophilic (Nsp9-Unknown) protein-protein interactions.

Conventional proteomic approaches measure the averaged signal from mixed cell populations or bulk tissues, leading to the dilution of signals arising from subpopulations of cells that might serve as important biomarkers. Recent developments in bottom-up proteomics have enabled spatial mapping of cellular heterogeneity in tissue microenvironments. However, bottom-up proteomics cannot unambiguously define and quantify proteoforms, which are intact (i.e. functional) forms of proteins capturing genetic variations, alternatively spliced transcripts and post-translational modifications. Herein, we described a spatially resolved top-down proteomics (TDP) platform for proteoform identification and quantitation directly from tissue sections. The spatial TDP platform consisted of a nanoPOTS (nanodroplet Processing in One pot for Trace Samples)-based sample preparation system and an LCM (laser capture microdissection)-based cell isolation system. We improved the nanoPOTS sample preparation by adding benzonase in the extraction buffer to enhance the coverage of nucleus proteins. Using ∼200 cultured cells as test samples, this approach increased total proteoform identifications from 493 to 700; with newly identified proteoforms primarily corresponding to nuclear proteins. To demonstrate the spatial TDP platform in tissue samples, we analyzed LCM-isolated tissue voxels from rat brain cortex and hypothalamus regions. We quantified 509 proteoforms within the union of TopPIC and TDPortal identifications to match with features from ProMex. Several proteoforms corresponding to the same gene exhibited mixed abundance profiles between two tissue regions, suggesting potential PTM-specific spatial distributions. The spatial TDP workflow has prospects for biomarker discovery at proteoform level from small tissue sections.

Protein encoding genes can undergo modifications posttranscriptionally and posttranslationally, yielding many different "proteoforms." The chemical diversity of such modifications is known to be important biomarkers of function within biological systems but is not completely understood. Top-down mass spectrometry is a valuable tool for the characterization of proteoforms, especially for histones that have complex combinations of posttranslational modifications (PTMs). In this chapter, we present a top-down liquid chromatography-mass spectrometry experimental and data analysis workflow for the identification of novel, unexpected modifications on histones. Proteoforms of interest are first discovered using the "open" modification search in TopPIC. Then target proteoforms are manually confirmed using the data visualization tool-LcMsSpectator, part of the Informed-Proteomics package. The workflow can be very helpful in targeted PTM analysis and can be expanded to other types of proteins for discovery of unknown PTMs.

Scope: A detailed protocol entailing the calibration, operation, and verification of the custom UHMR Q Exactive HF Orbitrap with a Spectroglyph EP-MALDI-2 source for high-spatial resolution imaging. This entails various procedures for instrument calibration and checks for the MALDI source and spectrometer for the intact proteoform analyses at near-cellular spatial resolution (> 15 µm) for human tissue sections without oversampling the tissue. Expected Outcomes: Creation of several dozen ion images which correspond to proteoforms visually localized within physiological regions of the profiled tissue sections.

The protocol describes how to use laser capture microdissection (LCM) to cut small regions of interest (~200-300 μm) from tissue sections. This is followed by top down proteomics analysis by liquid chromatography - mass spectrometry (LC-MS).

There are three major classes of the enzyme Glutamine Synthetase (GS), denoted as GSI, GSII and GSIII in both prokaryotes and eukaryotes, that share widely conserved active-site residues but vary in average protein length, oligomeric assembly state and cofactor requirements. Key structures have been determined for all prokaryotic and eukaryotic GS classes except the GSIII class of eukaryotic origin, where even basic questions about its functional oligomeric state are unresolved. Here, we report the 3.2Å cryo-EM structure of eukaryotic GSIII from the ancient organism of green algal lineage Ostreococcus tauri. Using a combined structural and biochemical approach, we demonstrate that O. tauri GSIII self-assembles and functions exclusively as a single hexameric ring. This is unlike most GSs in other classes, where they form double-stacked ring assemblies instead. Major structural differences in the ring-ring stacking across the classes suggest evolutionary pressure may have favored higher-order interactions that might be beneficial but are not necessarily required for the catalytic GS performance.

In biology, it can be argued that if the genome contains the script for a cell's life cycle, then the proteome constitutes an ensemble cast of actors that brings these instructions to life. Their interactions with each other, co-factors, ligands, substrates, and so on, are key to understanding nearly any biological process. Mass spectrometry is well established as the method of choice to determine protein primary structure and location of post-translational modifications. In recent years, top-down fragmentation of intact proteins has been increasingly combined with ionisation of noncovalent assemblies under non-denaturing conditions, i.e., native mass spectrometry. Sequence, post-translational modifications, ligand/metal binding, protein folding, and complex stoichiometry can thus all be probed directly. Here, we review recent developments in this new and exciting field of research. While this work is written primarily from a mass spectrometry perspective, it is targeted to all bioanalytical scientists who are interested in applying these methods to their own biochemistry and chemical biology research., Top-down mass spectrometry techniques break up native proteins and complexes to reveal all levels of structural information.

Top-down proteomics is a powerful tool for characterizing genetic variations and post-translational modifications at intact protein level. However, one significant technical gap of top-down proteomics is the inability to analyze a low amount of biological samples, which limits its access to isolated rare cells, fine needle aspiration biopsies, and tissue substructures. Herein, we developed an ultrasensitive top-down platform by incorporating a microfluidic sample preparation system, termed nanoPOTS (nanodroplet processing in one pot for trace samples), into a top-down proteomic workflow. A unique combination of a nonionic detergent dodecyl-β-d-maltopyranoside (DDM) with urea as protein extraction buffer significantly improved both protein extraction efficiency and sample recovery. We hypothesize that the DDM detergent improves protein recovery by efficiently reducing nonspecific adsorption of intact proteins on container surfaces, while urea serves as a strong denaturant to disrupt noncovalent complexes and release intact proteins for downstream analysis. The nanoPOTS-based top-down platform reproducibly and quantitatively identified ∼170 to ∼620 proteoforms from ∼70 to ∼770 HeLa cells containing ∼10 to ∼115 ng of total protein. A variety of post-translational modifications including acetylation, myristoylation, and iron binding were identified using only less than 800 cells. We anticipate the nanoPOTS top-down proteomics platform will be broadly applicable in biomedical research, particularly where clinical specimens are not available in amounts amenable to standard workflows.