Protocol to analyze 1D and 2D mass spectrometry data from glioblastoma tissues for cancer diagnosis and immune cell identification.

In context of cancer diagnosis-based mass spectrometry (MS), the classification model created is crucial. Moreover, exploration of immune cell infiltration in tissues can offer insights within the tumor microenvironment. Here, we present a protocol to analyze 1D and 2D MS data from glioblastoma tissues for cancer diagnosis and immune cells identification. We describe steps for training the most optimal model and cross-validating it, for discovering robust biomarkers and obtaining their corresponding boxplots as well as creating an immunoscore based on MS-imaging data. For complete details on the use and execution of this protocol, please refer to Zirem et al.1.

The impact and future of artificial intelligence in medical genetics and molecular medicine: an ongoing revolution.

Artificial intelligence (AI) platforms have emerged as pivotal tools in genetics and molecular medicine, as in many other fields. The growth in patient data, identification of new diseases and phenotypes, discovery of new intracellular pathways, availability of greater sets of omics data, and the need to continuously analyse them have led to the development of new AI platforms. AI continues to weave its way into the fabric of genetics with the potential to unlock new discoveries and enhance patient care. This technology is setting the stage for breakthroughs across various domains, including dysmorphology, rare hereditary diseases, cancers, clinical microbiomics, the investigation of zoonotic diseases, omics studies in all medical disciplines. AI's role in facilitating a deeper understanding of these areas heralds a new era of personalised medicine, where treatments and diagnoses are tailored to the individual's molecular features, offering a more precise approach to combating genetic or acquired disorders. The significance of these AI platforms is growing as they assist healthcare professionals in the diagnostic and treatment processes, marking a pivotal shift towards more informed, efficient, and effective medical practice. In this review, we will explore the range of AI tools available and show how they have become vital in various sectors of genomic research supporting clinical decisions.

In Vivo and Real-Time Metabolic Profiling of Plant-Microbe Interactions in Leaves, Stems, and Roots of Bacterially Inoculated Chardonnay Plantlets using SpiderMass.

There is growing interest in limiting the use of fungicides and implementing innovative, environmentally friendly strategies, such as the use of beneficial bacteria-triggered immunity, to protect grapevines from natural pathogens. Therefore, we need rapid and innovative ways to translate the knowledge of the molecular mechanisms underlying the activation of grapevine defenses against pathogens to induced resistance. Here, we have implemented an in vivo minimally invasive approach to study the interaction between plants and beneficial bacteria based on metabolic signatures. Paraburkholderia phytofirmans strain PsJN and PsJN-grapevine were used as bacterial and plant-bacterium interaction models, respectively. Using an innovative tool, SpiderMass, based on water-assisted laser desorption ionization with an IR microsampling probe, we simultaneously detect metabolic and lipidomic species. A metabolomic spectrum was thus generated, which was used to build a library and identify the most variable and discriminative peaks between the two conditions. We then showed that caftaric acid (m/z 311.04), caftaric acid dimer (m/z 623.09), derived caftaric acid (m/z 653.15), and quercetin-O-glucuronide tended to accumulate in grapevine leaves after root bacterization with PsJN. In addition, together with these phenolic messengers, we identified lipid biomarkers such as palmitic acid, linoleic acid, and α-linoleic acid as important messengers of enhanced defense mechanisms in Chardonnay plantlets. Taken together, SpiderMass is the next-generation methodology for studying plant-microorganism metabolic interactions with the prospect of in vivo real-time analysis in viticulture.

A co-culture system of macrophages with breast cancer tumoroids to study cell interactions and therapeutic responses.

3D tumoroids have revolutionized in vitro/ex vivo cancer biology by recapitulating the complex diversity of tumors. While tumoroids provide new insights into cancer development and treatment response, several limitations remain. As the tumor microenvironment, especially the immune system, strongly influences tumor development, the absence of immune cells in tumoroids may lead to inappropriate conclusions. Macrophages, key players in tumor progression, are particularly challenging to integrate into the tumoroids. In this study, we established three optimized and standardized methods for co-culturing human macrophages with breast cancer tumoroids: a semi-liquid model and two matrix-embedded models tailored for specific applications. We then tracked interactions and macrophage infiltration in these systems using flow cytometry and light sheet microscopy and showed that macrophages influenced not only tumoroid molecular profiles but also chemotherapy response. This underscores the importance of increasing the complexity of 3D models to more accurately reflect in vivo conditions.

Real-time glioblastoma tumor microenvironment assessment by SpiderMass for improved patient management.

Glioblastoma is a highly heterogeneous and infiltrative form of brain cancer associated with a poor outcome and limited therapeutic effectiveness. The extent of the surgery is related to survival. Reaching an accurate diagnosis and prognosis assessment by the time of the initial surgery is therefore paramount in the management of glioblastoma. To this end, we are studying the performance of SpiderMass, an ambient ionization mass spectrometry technology that can be used in vivo without invasiveness, coupled to our recently established artificial intelligence pipeline. We demonstrate that we can both stratify isocitrate dehydrogenase (IDH)-wild-type glioblastoma patients into molecular sub-groups and achieve an accurate diagnosis with over 90% accuracy after cross-validation. Interestingly, the developed method offers the same accuracy for prognosis. In addition, we are testing the potential of an immunoscoring strategy based on SpiderMass fingerprints, showing the association between prognosis and immune cell infiltration, to predict patient outcome.

The colibactin-producing Escherichia coli alters the tumor microenvironment to immunosuppressive lipid overload facilitating colorectal cancer progression and chemoresistance.

Intratumoral bacteria flexibly contribute to cellular and molecular tumor heterogeneity for supporting cancer recurrence through poorly understood mechanisms. Using spatial metabolomic profiling technologies and 16SrRNA sequencing, we herein report that right-sided colorectal tumors are predominantly populated with Colibactin-producing Escherichia coli (CoPEC) that are locally establishing a high-glycerophospholipid microenvironment with lowered immunogenicity. It coincided with a reduced infiltration of CD8+ T lymphocytes that produce the cytotoxic cytokines IFN-γ where invading bacteria have been geolocated. Mechanistically, the accumulation of lipid droplets in infected cancer cells relied on the production of colibactin as a measure to limit genotoxic stress to some extent. Such heightened phosphatidylcholine remodeling by the enzyme of the Land's cycle supplied CoPEC-infected cancer cells with sufficient energy for sustaining cell survival in response to chemotherapies. This accords with the lowered overall survival of colorectal patients at stage III-IV who were colonized by CoPEC when compared to patients at stage I-II. Accordingly, the sensitivity of CoPEC-infected cancer cells to chemotherapies was restored upon treatment with an acyl-CoA synthetase inhibitor. By contrast, such metabolic dysregulation leading to chemoresistance was not observed in human colon cancer cells that were infected with the mutant strain that did not produce colibactin (11G5∆ClbQ). This work revealed that CoPEC locally supports an energy trade-off lipid overload within tumors for lowering tumor immunogenicity. This may pave the way for improving chemoresistance and subsequently outcome of CRC patients who are colonized by CoPEC.

OpenProt 2.0 builds a path to the functional characterization of alternative proteins.

The OpenProt proteogenomic resource (https://www.openprot.org/) provides users with a complete and freely accessible set of non-canonical or alternative open reading frames (AltORFs) within the transcriptome of various species, as well as functional annotations of the corresponding protein sequences not found in standard databases. Enhancements in this update are largely the result of user feedback and include the prediction of structure, subcellular localization, and intrinsic disorder, using cutting-edge algorithms based on machine learning techniques. The mass spectrometry pipeline now integrates a machine learning-based peptide rescoring method to improve peptide identification. We continue to help users explore this cryptic proteome by providing OpenCustomDB, a tool that enables users to build their own customized protein databases, and OpenVar, a genomic annotator including genetic variants within AltORFs and protein sequences. A new interface improves the visualization of all functional annotations, including a spectral viewer and the prediction of multicoding genes. All data on OpenProt are freely available and downloadable. Overall, OpenProt continues to establish itself as an important resource for the exploration and study of new proteins.

Multi-Omics Data Integration Reveals Sex-Dependent Hippocampal Programming by Maternal High-Fat Diet during Lactation in Adult Mouse Offspring.

Early-life exposure to high-fat diets (HF) can program metabolic and cognitive alterations in adult offspring. Although the hippocampus plays a crucial role in memory and metabolic homeostasis, few studies have reported the impact of maternal HF on this structure. We assessed the effects of maternal HF during lactation on physiological, metabolic, and cognitive parameters in young adult offspring mice. To identify early-programming mechanisms in the hippocampus, we developed a multi-omics strategy in male and female offspring. Maternal HF induced a transient increased body weight at weaning, and a mild glucose intolerance only in 3-month-old male mice with no change in plasma metabolic parameters in adult male and female offspring. Behavioral alterations revealed by a Barnes maze test were observed both in 6-month-old male and female mice. The multi-omics strategy unveiled sex-specific transcriptomic and proteomic modifications in the hippocampus of adult offspring. These studies that were confirmed by regulon analysis show that, although genes whose expression was modified by maternal HF were different between sexes, the main pathways affected were similar with mitochondria and synapses as main hippocampal targets of maternal HF. The effects of maternal HF reported here may help to better characterize sex-dependent molecular pathways involved in cognitive disorders and neurodegenerative diseases.

Fallopian tube lesions as potential precursors of early ovarian cancer: a comprehensive proteomic analysis.

Ovarian cancer is the leading cause of death from gynecologic cancer worldwide. High-grade serous carcinoma (HGSC) is the most common and deadliest subtype of ovarian cancer. While the origin of ovarian tumors is still debated, it has been suggested that HGSC originates from cells in the fallopian tube epithelium (FTE), specifically the epithelial cells in the region of the tubal-peritoneal junction. Three main lesions, p53 signatures, STILs, and STICs, have been defined based on the immunohistochemistry (IHC) pattern of p53 and Ki67 markers and the architectural alterations of the cells, using the Sectioning and Extensively Examining the Fimbriated End Protocol. In this study, we performed an in-depth proteomic analysis of these pre-neoplastic epithelial lesions guided by mass spectrometry imaging and IHC. We evaluated specific markers related to each preneoplastic lesion. The study identified specific lesion markers, such as CAVIN1, Emilin2, and FBLN5. We also used SpiderMass technology to perform a lipidomic analysis and identified the specific presence of specific lipids signature including dietary Fatty acids precursors in lesions. Our study provides new insights into the molecular mechanisms underlying the progression of ovarian cancer and confirms the fimbria origin of HGSC.

Comparing MS imaging of lipids by WALDI and MALDI: two technologies for evaluating a common ground truth in MS imaging.

In this study, we conducted a direct comparison of water-assisted laser desorption ionization (WALDI) and matrix-assisted laser desorption ionization (MALDI) mass spectrometry imaging, with MALDI serving as the benchmark for label-free molecular tissue analysis in biomedical research. Specifically, we investigated the lipidomic profiles of several biological samples and calculated the similarity of detected peaks and Pearson's correlation of spectral profile intensities between the two techniques. We show that, overall, MALDI MS and WALDI MS present very close lipidomic analyses and that the highest similarity is obtained for the norharmane MALDI matrix. Indeed, for norharmane in negative ion mode, the lipidomic spectra revealed 100% similarity of detected peaks and over 0.90 intensity correlation between both technologies for five samples. The MALDI-MSI positive ion lipid spectra displayed more than 83% similarity of detected peaks compared to those of WALDI-MSI. However, we observed a lower percentage (77%) of detected peaks when comparing WALDI-MSI with MALDI-MSI due to the rich WALDI-MSI lipid spectra. Despite this difference, the global lipidomic spectra showed high consistency between the two technologies, indicating that they are governed by similar processes. Thanks to this similarity, we can increase datasets by including data from both modalities to either co-train classification models or obtain cross-interrogation.

Development of a novel apigenin prodrug programmed for alkaline-phosphatase instructed self-inhibition to combat cancer.

Elevated levels of alkaline phosphatase (ALP) in the tumor microenvironment (TME) are a hallmark of cancer progression and thus inhibition of ALP could serve as an effective approach against cancer. Herein, we developed a novel prodrug approach to tackle cancer that bears self-inhibiting alkaline phosphatase-responsiveness properties that can enhance at the same time the solubility of the parent compound. To probe this novel concept, we selected apigenin as the cytotoxic agent since we first unveiled, that it directly interacts and inhibits ALP activity. Consequently, we rationally designed and synthesized, using a self-immolative linker, an ALP responsive apigenin-based phosphate prodrug, phospho-apigenin. Phospho-apigenin markedly increased the stability of the parent compound apigenin. Furthermore, the prodrug exhibited enhanced antiproliferative effect in malignant cells with elevated ALP levels, compared to apigenin. This recorded potency of the developed prodrug was further confirmed in vivo where phospho-apigenin significantly suppressed by 52.8% the growth of PC-3 xenograft tumors.Communicated by Ramaswamy H. Sarma.

Early Diagnosis: End-to-End CNN-LSTM Models for Mass Spectrometry Data Classification.

Liquid chromatography-mass spectrometry (LC-MS) is a powerful method for cell profiling. The use of LC-MS technology is a tool of choice for cancer research since it provides molecular fingerprints of analyzed tissues. However, the ubiquitous presence of noise, the peaks shift between acquisitions, and the huge amount of information owing to the high dimensionality of the data make rapid and accurate cancer diagnosis a challenging task. Deep learning (DL) models are not only effective classifiers but are also well suited to jointly learn feature representation and classification tasks. This is particularly relevant when applied to raw LC-MS data and hence avoid the need for costly preprocessing and complicated feature selection. In this study, we propose a new end-to-end DL methodology that addresses all of the above challenges at once, while preserving the high potential of LC-MS data. Our DL model is designed to early discriminate between tumoral and normal tissues. It is a combination of a convolutional neural network (CNN) and a long short-term memory (LSTM) Network. The CNN network allows for significantly reducing the high dimensionality of the data while learning spatially relevant features. The LSTM network enables our model to capture temporal patterns. We show that our model outperforms not only benchmark models but also state-of-the-art models developed on the same data. Our framework is a promising strategy for improving early cancer detection during a diagnostic process.

Heimdall, an alternative protein issued from a ncRNA related to kappa light chain variable region of immunoglobulins from astrocytes: a new player in neural proteome.

The dogma "One gene, one protein" is clearly obsolete since cells use alternative splicing and generate multiple transcripts which are translated into protein isoforms, but also use alternative translation initiation sites (TISs) and termination sites on a given transcript. Alternative open reading frames for individual transcripts give proteins originate from the 5'- and 3'-UTR mRNA regions, frameshifts of mRNA ORFs or from non-coding RNAs. Longtime considered as non-coding, recent in-silico translation prediction methods enriched the protein databases allowing the identification of new target structures that have not been identified previously. To gain insight into the role of these newly identified alternative proteins in the regulation of cellular functions, it is crucial to assess their dynamic modulation within a framework of altered physiological modifications such as experimental spinal cord injury (SCI). Here, we carried out a longitudinal proteomic study on rat SCI from 12 h to 10 days. Based on the alternative protein predictions, it was possible to identify a plethora of newly predicted protein hits. Among these proteins, some presented a special interest due to high homology with variable chain regions of immunoglobulins. We focus our interest on the one related to Kappa variable light chains which is similarly highly produced by B cells in the Bence jones disease, but here expressed in astrocytes. This protein, name Heimdall is an Intrinsically disordered protein which is secreted under inflammatory conditions. Immunoprecipitation experiments showed that the Heimdall interactome contained proteins related to astrocyte fate keepers such as "NOTCH1, EPHA3, IPO13" as well as membrane receptor protein including "CHRNA9; TGFBR, EPHB6, and TRAM". However, when Heimdall protein was neutralized utilizing a specific antibody or its gene knocked out by CRISPR-Cas9, sprouting elongations were observed in the corresponding astrocytes. Interestingly, depolarization assays and intracellular calcium measurements in Heimdall KO, established a depolarization effect on astrocyte membranes KO cells were more likely that the one found in neuroprogenitors. Proteomic analyses performed under injury conditions or under lipopolysaccharides (LPS) stimulation, revealed the expression of neuronal factors, stem cell proteins, proliferation, and neurogenesis of astrocyte convertor factors such as EPHA4, NOTCH2, SLIT3, SEMA3F, suggesting a role of Heimdall could regulate astrocytic fate. Taken together, Heimdall could be a novel member of the gatekeeping astrocyte-to-neuroprogenitor conversion factors.

Ambient Mass Spectrometry Imaging by Water-Assisted Laser Desorption/Ionization for Ex Vivo and in Vivo Applications.

Water-assisted laser desorption/ionization mass spectrometry (WALDI-MS), also known as SpiderMass, is an emerging ambient ionization technique for in vivo and real-time analysis. It employs a remote infrared (IR) laser tuned to excite the most intense vibrational band (O-H) of water. The water molecules act as an endogenous matrix leading to the desorption/ionization of a variety of biomolecules from tissues, particularly metabolites and lipids. WALDI-MS was recently advanced into an imaging modality for ex vivo 2D sections and 3D in vivo real-time imaging. Here, we describe the methodological aspects for performing 2D and 3D imaging experiments with WALDI-MSI and the parameters for optimizing the image acquisition.

Protocol to identify human subcellular alternative protein interactions using cross-linking mass spectrometry.

Since the start of mass-spectrometry-based proteomics, proteins from non-referenced open reading frames or alternative proteins (AltProts) have been overlooked. Here, we present a protocol to identify human subcellular AltProt and decipher some interactions using cross-linking mass spectrometry. We describe steps for cell culture, in cellulo cross-link, subcellular extraction, and sequential digestion. We then detail both liquid chromatography-tandem mass spectrometry and cross-link data analyses. The implementation of a single workflow allows the non-targeted identification of signaling pathways involving AltProts. For complete details on the use and execution of this protocol, please refer to Garcia-del Rio et al.1.

Astrocytes express aberrant immunoglobulins as putative gatekeeper of astrocytes to neuronal progenitor conversion.

Using multi-omics analyses including RNAseq, RT-PCR, RACE-PCR, and shotgun proteomic with enrichment strategies, we demonstrated that newborn rat astrocytes produce neural immunoglobulin constant and variable heavy chains as well as light chains. However, their edification is different from the ones found in B cells and they resemble aberrant immunoglobulins observed in several cancers. Moreover, the complete enzymatic V(D)J recombination complex has also been identified in astrocytes. In addition, the constant heavy chain is also present in adult rat astrocytes, whereas in primary astrocytes from human fetus we identified constant and variable kappa chains as well as the substitution lambda chains known to be involved in pre-B cells. To gather insights into the function of these neural IgGs, CRISPR-Cas9 of IgG2B constant heavy chain encoding gene (Igh6), IgG2B overexpression, proximal labeling of rat astrocytes IgG2B and targets identification through 2D gels were performed. In Igh6 KO astrocytes, overrepresentation of factors involved in hematopoietic cells, neural stem cells, and the regulation of neuritogenesis have been identified. Moreover, overexpression of IgG2B in astrocytes induces the CRTC1-CREB-BDNF signaling pathway known to be involved in gliogenesis, whereas Igh6 KO triggers the BMP/YAP1/TEAD3 pathway activated in astrocytes dedifferentiation into neural progenitors. Proximal labeling experiments revealed that IgG2B is N-glycosylated by the OST complex, addressed to vesicle membranes containing the ATPase complex, and behaves partially like CD98hc through its association with LAT1. These experiments also suggest that proximal IgG2B-LAT1 interaction occurs concomitantly with MACO-1 and C2CD2L, at the heart of a potentially novel cell signaling platform. Finally, we demonstrated that these chains are synthesized individually and associated to recognize specific targets. Indeed, intermediate filaments Eif4a2 and Pdia6 involved in astrocyte fate constitute targets for these neural IgGs. Taken together, we hypothese that neural aberrant IgG chains may act as gatekeepers of astrocytes' fate.

Employing non-targeted interactomics approach and subcellular fractionation to increase our understanding of the ghost proteome.

Eukaryotic mRNA has long been considered monocistronic, but nowadays, alternative proteins (AltProts) challenge this tenet. The alternative or ghost proteome has largely been neglected and the involvement of AltProts in biological processes. Here, we used subcellular fractionation to increase the information about AltProts and facilitate the detection of protein-protein interactions by the identification of crosslinked peptides. In total, 112 unique AltProts were identified, and we were able to identify 220 crosslinks without peptide enrichment. Among these, 16 crosslinks between AltProts and Referenced Proteins (RefProts) were identified. We further focused on specific examples such as the interaction between IP_2292176 (AltFAM227B) and HLA-B, in which this protein could be a potential new immunopeptide, and the interactions between HIST1H4F and several AltProts which can play a role in mRNA transcription. Thanks to the study of the interactome and the localization of AltProts, we can reveal more of the importance of the ghost proteome.

Prophylactic Radical Fimbriectomy with Delayed Oophorectomy in Women with a High Risk of Developing an Ovarian Carcinoma: Results of a Prospective National Pilot Study.

Risk-reducing salpingo-oophorectomy is the gold standard for the prophylaxis of ovarian cancer in high-risk women. Due to significant adverse effects, 20-30% of women delay or refuse early oophorectomy. This prospective pilot study (NCT01608074) aimed to assess the efficacy of radical fimbriectomy followed by a delayed oophorectomy in preventing ovarian and pelvic invasive cancer (the primary endpoint) and to evaluate the safety of both procedures. The key eligibility criteria were pre-menopausal women ≥35 years with a high risk of ovarian cancer who refused a risk-reducing salpingo-oophorectomy. All the surgical specimens were subjected to the SEE-FIM protocol. From January 2012 to October 2014, 121 patients underwent RF, with 51 in an ambulatory setting. Occult neoplasia was found in two cases, with one tubal high-grade serous ovarian carcinoma. Two patients experienced grade 1 intraoperative complications. No early or delayed grade ≥3 post-operative complications occurred. After 7.3 years of median follow-up, no cases of pelvic invasive cancer have been noted. Three of the fifty-two patients developed de novo breast cancer. One BRCA1-mutated woman delivered twins safely. Twenty-five patients underwent menopause, including fifteen who had received chemotherapy for breast cancer, and twenty-three underwent menopause before the delayed oophorectomy, while two did not undergo a delayed oophorectomy at all. Overall, 46 women underwent a delayed oophorectomy. No abnormalities were found in any delayed oophorectomy specimens. Radical fimbriectomy followed by delayed oophorectomy appears to be a safe and well-tolerated risk-reducing approach, which avoids early menopause for patients with a high risk of breast and ovarian cancer.