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McDonnell, K.

Publications and source records attributed to McDonnell, K..

5 recordsLinked to original sources

How to design 1000-plex mass tags using the differential mass defect

Multiplexing samples in mass spectrometry-based proteomics has long been accomplished by iso-topologues of small molecules. These chemically-identical "mass tags" conjugate to peptides to encode samples with different mass offsets for parallel analysis. The current state-of-the-art for multiplexing with non-isobaric mass tags was recently improved from 3-plex to 9-plex, but what is the largest plex size that can be reasonably achieved with current technology? A full answer to this question requires evaluating current mass spectrometry hardware, facets of which have been well-investigated by others. However, it may be underappreciated that multiplexing 1000s of samples with mass tags does not actually require 1000s of isotopes, or 1000s of synthesis steps to create. Non-intuitively, high plex mass tags can require relatively few different isotopes. The focus of this exposition is to characterize the potential of the differential mass defect to create tens to over a thousand isotopologues of small molecules and how careful combinations of these small molecules can combinatorially scale the plex size to minimize synthetic steps. Importantly, we show that plex sizes in the hundreds, an order of magnitude greater than state-of-the-art, are achievable using molecules comparable in size to existing commercial tags, and that going beyond hundreds may require larger molecules. Approaches to achieve high-plex proteomics will almost certainly require using the differential mass defect, so we hope this exposition serves to accelerate progress in reagent development to achieve high plex proteomics.

systems biology↗

PSMtags improve peptide sequencing and throughput in sensitive proteomics

Mass spectrometry-based proteomics enables comprehensive characterization of protein abundance, function, and interactions. Label-free approaches are simple to implement but challenging to scale to thousands of samples per day. Multiplexed techniques, such as plexDIA, can address these limitations but remain restricted by the lack of mass tags optimized for data-independent acquisition (DIA) workflows. Here, we present a systematic approach screening a library of 576 compounds that identifies several small molecules that, when conjugated to peptides, improve their detection and sequence identification by mass spectrometry. The lead molecule, PSMtag, substantially increases the detection of fragment b-ions, which increases the confidence of sequence identification and enhances de novo sequencing. PSMtags allow 9-plexDIA, using only stable isotopes of carbon, oxygen and nitrogen. As a result, it allows simultaneously increasing proteome coverage and sample throughput for plexDIA workflows without compromising quantitative accuracy. We demonstrate 240 samples-per-day with 9-plexDIA, while acquiring 28,359 protein data points in the same time label-free methods acquire 4,340. Our approach constitutes an expandable framework for designing mass tags to overcome existing limitations in multiplexed proteomics and provides plexDIA reagents capable of analyzing over 1,000 samples per day when using 10 minute runs. By facilitating higher throughput and improved identification, this innovation holds significant potential for accelerating proteomic studies across diverse biological and clinical applications.

bioengineering↗

JMod: Joint modeling of mass spectra for empowering multiplexed DIA proteomics

Parallelization of data acquisition substantially increases the throughput of mass spectrometry-based proteomics. However, parallelization also increases the density of mass spectra and consequently the overlap between ions, frustrating their analysis. To improve sequence identification and quantification from such spectra, we developed an open-source software for Joint Modeling of mass spectra (JMod). JMod models overlapping peaks as linear superpositions of their components in both MS1 and MS2 space, which permits multiplexed DIA with smaller mass offsets to increase the multiplexing capacity and thus proteomics throughput for a given plexDIA tag. This enables 9-plexDIA using 2 Da offset PSMtags, increasing throughput 9-fold while preserving quantitative accuracy and coverage depth. Furthermore, we use JMod to deconvolve simultaneous labeling by mass tags and heavy amino acids, thus increasing the throughput of metabolic pulse experiments measuring protein synthesis and degradation rates in single cells from mouse liver. By supporting enhanced decoding of highly multiplexed DIA spectra, JMod provides an open and flexible software that increases the throughput of sensitive proteomics.

bioinformatics↗

Increasing mass spectrometry throughput using time-encoded sample multiplexing

Liquid chromatography-mass spectrometry (LC-MS) can enable precise and accurate quantification of analytes at high-sensitivity, but the rate at which samples can be analyzed remains limiting. Throughput can be increased by multiplexing samples in the mass domain with plexDIA, yet multiplexing along one dimension will only linearly scale throughput with plex. To enable combinatorial-scaling of proteomics throughput, we developed a complementary multiplexing strategy in the time domain, termed timePlex. timePlex staggers and overlaps the separation periods of individual samples. This strategy is orthogonal to isotopic multiplexing, which enables combinatorial multiplexing in mass and time domains when paired together, and thus multiplicatively increased throughput. We demonstrate this with 3-timePlex and 3-plexDIA, enabling the multiplexing of 9 samples per LC-MS run, and 3-timePlex and 9-plexDIA exceeding 500 samples / day with a combinatorial 27-plex. Crucially, timePlex supports sensitive analyses, including of single cells. These results establish timePlex as a methodology for label-free multiplexing and combinatorial scaling of the throughput of LC-MS proteomics. We project this combined approach will eventually enable an increase in throughput exceeding 1,000 samples / day. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=97 SRC="FIGDIR/small/655515v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@1c1d799org.highwire.dtl.DTLVardef@1319339org.highwire.dtl.DTLVardef@1b8bf4borg.highwire.dtl.DTLVardef@16d9e9_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

Conditional activation of NK cell function using chemically synthetic constrained bicyclic peptides directed against NKp46 and tumor-expressed antigens

Natural killer (NK) cells have the unique potential to recognize and kill tumor cells independently of MHC-I presentation of antigens, as well as to secrete cytokines that engage adaptive anti-tumor immunity and the function of cytolytic T cells. We have discovered and characterized chemically synthetic, constrained bicyclic peptides that bind with high affinity and specificity to NKp46, an activating receptor expressed selectively on NK cells in the tumor microenvironment. Chemical coupling to other bicyclic peptides specific for the tumor antigens EphA2 or MT-1 created NKp46 agonists whose function was completely conditional on binding to the tumor antigen. These chemical conjugates effectively convert the tumor antigen into a "kill me" signal for NK cells. Not only did these newly created tumor-immune cell agonists (TICAs) direct potent and efficient killing of human tumor cells by primary human NK cells in vitro, but they also caused secretion of the pro-inflammatory cytokines TNF and IFN{gamma}. Importantly, the TICAs directed production of FLT3 ligand, an essential mitogen for conventional dendritic cells which are central to the development of anti-tumor immunity in cancer. We illustrate the TICA-directed interaction of NK cells with tumor cells using confocal microscopy and we show that TICAs enable sustained function over multiple rounds of killing. These novel tools are well positioned to harness the potential of NK cells in the treatment of cancer.

cancer biology↗