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Spike-in Proteome to enhance Data-Independent Acquisition for Thermal Proteome Profiling

ABSTRACT: Target deconvolution interactions is essential for elucidating the molecular mechanisms, therapeutic efficacy and off-target toxicity of small molecule drugs. Thermal proteome profiling (TPP) is a robust popular method for identifying drug-protein interactions. Nevertheless, classical implementation of TPP using isobaric labelling of peptides is tedious, time-consuming and costly. This prompt the adoption of label-free approach with data-independent acquisition (DIA), but with substantial compromise in protein coverage and precision. To address these shortcomings, we improvise a spike-in proteome strategy for DIA with TPP to counteract the reduction in protein quantity following sample heating. Protein coverage, data completeness and quantification precision are significantly improved as result. Additionally, a calibration algorithm was developed to correct for spike-in effects on fold changes. The integration of DIA-TPP with the Matrix-Augmented Pooling Strategy (MAPS) to increase experiment throughput demonstrates performance comparable to existing TMT-TPP-MAPS. With this spike-in proteome strategy, we also successfully identified the thermal stabilization of CA13 by dorzolamide hydrochloride, as well as GSTZ1 and TDP1 of opicapone that eluded detection without the spike-in proteome.

INSTRUMENT(S): Orbitrap Exploris 480

ORGANISM(S): Homo Sapiens (human)

SUBMITTER: qiqi Wang

LAB HEAD: Chris Soon Heng Tan

PROVIDER: PXD056816 | Pride | 2025-03-11

REPOSITORIES: Pride

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Project description:Background: The early stages of D. melanogaster embryogenesis involve cell migration and pattern formation, and lead to the formation of three germ layers (the ectoderm, mesoderm and endoderm). These developmental events are controlled by differential gene activity. In the current study we used a suppressive subtractive hybridization (SSH) procedure to identify a group of genes potentially involved in D. melanogaster early embryogenesis and to study the temporal activity of developmentally regulated genes at five different intervals covering 12 stages of embryogenesis. Results: Macroarrays were constructed to confirm induction of expression and determine the temporal profile of isolated subtracted cDNAs during embryo development. We identified a set of 118 genes that significantly increased their expression levels at least at one developmental interval compared with the reference interval. 53.4% of them have a phenotype and/or molecular function reported in the literature, whereas 46.6% are essentially uncharacterized. Clustering analysis revealed demarcated transcript groups with maximum gene activity at distinct developmental intervals. In situ hybridization assays were carried out on 30 of the transcripts and of these, 19 (63%) proved to have restricted expression patterns. 11 of the uncharacterized genes that exhibited temporal and spatially restricted patterns of expression in developing embryos encode putative secreted and transmembrane proteins. For three of them we validated our protein sequence predictions by expressing their cDNAs in S2R+ cells and analyze the subcellular distribution of recombinant proteins. Conclusions: Our data provides a list of developmentally regulated D. melanogaster genes and their expression profiles during embryogenesis, including novel information on the temporal and spatial expression patterns of several previously uncharacterized genes. In particular, we recovered a significant number of novel genes encoding putative secreted and transmembrane proteins, suggesting new components of signalling pathways that might be incorporated within the existing regulatory networks controlling Drosophila embryogenesis; they are also good candidates for more functional targeted analyses. Keywords: Drosophila melanogaster, gastrulation, macroarray, developmental time course We generated a subtracted stage specific cDNA library representing genes differentially expressed between Drosophila melanogaster synciyial blastoderm and gastrula stages. Clones were spotted onto nylon membranes and hybridized against cDNA probes representing mRNA from embryos at five different developmental intervals, in order to obtain time course expression data covering the first 12 stages of Drosophila development. We designed macroarrays containing either 302 non-redundant cDNAs, including protein-coding sequences, ncRNA, transposons, introns and intergenic regions (membranes A, B and C) and the complete subtracted library (579 cDNAs, membrane D). Each clone was spotted in duplicate. The following controls were spotted onto membranes: (1) a fragment of the vector pBluescript II obtained by amplification with the T7 and SP6 universal primers; (2) several spots of DMSO 50% (solvent used); (3) PCR amplified fragment of genes serendipity Î±, twist, tinman, fog and snail as positive controls, and (4) actin, tubulin, and RP49 as housekeeping, and (5) four dilutions of a PCR amplified fragment from a B. subtilis dap cDNA (ATCC; number 87486), which hybridizes to an in vitro synthesized poly(A)-RNA that was added to the embryo RNA samples (dilution 1/200) prior to the labelling process and used as spike mRNA (Kane et al., 2000).

Project description:While the importance of random sequencing errors decreases at higher DNA or RNA sequencing depths, systematic sequencing errors (SSEs) dominate at high sequencing depths and can be difficult to distinguish from biological variants. These SSEs can cause base quality scores to underestimate the probability of error at certain genomic positions, resulting in false positive variant calls, particularly in mixtures such as samples with RNA editing, tumors, circulating tumor cells, bacteria, mitochondrial heteroplasmy, or pooled DNA. Most algorithms proposed for correction of SSEs require a training data set, which is typically either from a part of the data set being M-bM-^@M-^\recalibratedM-bM-^@M-^] (Genome Analysis ToolKit, or GATK) or from a separate data set with special characteristics (SysCall). Here, we combine the advantages of these approaches by adding synthetic RNA spike-in standards to human RNA, and use GATK to recalibrate base quality scores with reads mapped to the spike-in standards. Compared to conventional GATK recalibration that uses reads mapped to the genome, spike-ins improve the accuracy of Illumina base quality scores by a mean of 5 units, and by as much as 13 units M-BM- at CpG sites. In addition, since reads mapping to the genome are not used for recalibration, our method allows run-specific recalibration even for the many species without a comprehensive and accurate SNP database. We also use GATK with the spike-in standards to demonstrate that the Illumina RNA sequencing runs overestimate quality scores for AC, CC, GC, GG, and TC dinucleotides, while SOLiD has less dinucleotide SSEs but more SSEs for certain cycles. We conclude that using these DNA and RNA spike-in standards with GATK improves base quality score recalibration. Four human RNA samples with equimolar ERCC spike-in standards were sequenced on Illumina. Two human brain/liver/muscle RNA mixtures with dynamic range of ERCC spike-in standards were sequenced on SOLiD.

			Action	DRS
	DIA_1.raw	Raw
	DIA_10.raw	Raw
	DIA_11.raw	Raw
	DIA_12.raw	Raw
	DIA_13.raw	Raw

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Spike-in Proteome to enhance Data-Independent Acquisition for Thermal Proteome Profiling

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