Biology subjects

Wisztorski, M.

Publications and source records attributed to Wisztorski, M..

4 recordsLinked to original sources

bioRxiv2021

Towards high spatially resolved microproteomics using expansion microscopy

Expansion microscopy is an emerging approach for morphological examination of biological specimens at nanoscale resolution using conventional optical microscopy. To achieve physical separation of cell structures, tissues are embedded in a swellable polymer and expanded several folds in an isotropic manner. This work shows the development and optimization of physical tissue expansion as a new method for spatially resolved large scale proteomics. Herein, we established a novel method to enlarge the tissue section to be compatible with manual microdissection on regions of interest and to carry out MS-based proteomic analysis. A major issue in the Expansion microscopy is the loss of proteins information during the mechanical homogenization phase due to the use of Proteinase K. For isotropic expansion, different homogenization agents are investigated, both to maximize protein identification and to minimize protein diffusion. Better results are obtained with SDS. From a tissue section enlarge more than 3-fold, we have been able to manually cut out regions of 1mm in size, equivalent to 300{micro}m in their real size. We identified up to 655 proteins from a region corresponding to an average of 940 cells. This approach can be performed easily without any expensive sampling instrument. We demonstrated the compatibility of sample preparation for expansion microscopy and proteomic study in a spatial context. Abstract graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=115 SRC="FIGDIR/small/433765v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@14cc519org.highwire.dtl.DTLVardef@1201e36org.highwire.dtl.DTLVardef@19fa853org.highwire.dtl.DTLVardef@e4112a_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2020

Overall patient's survival of glioblastoma associated to molecular markers: a pan-proteomic prospective study

Molecular heterogeneities are a key feature of glioblastoma (GBM) pathology impeding patients stratification and leading to high discrepancies between patients mean survivals. Here, we established a molecular classification of GBM tumors using a pan-proteomic analysis. Then, we identified, from our proteomic data, 2 clusters of biomarkers associated with good or bad patient survival from 46 IDH wild-type GBMs. Three molecular groups have been identified and associated with systemic biology analyses. Group A tumors exhibit neurogenesis characteristics and tumorigenesis. Group B shows a strong immune cell signature and express poor prognosis markers while group C tumors are characterized by an anti-viral signature and tumor growth proteins. 124 proteins were found statistically different based on patients survival times, of which 10 are issued from alternative AltORF or non-coding RNA. After statistical analysis, a panel of markers associated to higher survival (PPP1R12A, RPS14, HSPD1 and LASP1) and another panel associated to lower survival (ALCAM, ANXA11, MAOB, IP_652563 and IGHM) has been validated by immunofluorescence. Taken together, our data will guide GBM prognosis and help to improve the current GBM classification by stratifying the patients and may open new opportunities for therapeutic development. SignificanceGlioblastoma are very heterogeneous tumors with median survivals usually inferior to 20 months. We conducted a pan-proteomics analysis of glioblastoma (GBM) in order to stratify GBM based on the molecular contained. Forty-six GBM cases were classified into three groups where proteins are involved in specific pathways i.e. the first group has a neurogenesis signature and is associated with a better prognosis while the second group of patients has an immune profile with a bad prognosis. The third group is more associated to tumorigenesis. We correlated these results with the TCGA data. Finally, we have identified 28 new prognostic markers of GBM and from these 28, a panel of 4 higher and 5 lower survival markers were validated. With these 9 markers in hand, now pathologist can stratify GBM patients and can guide the therapeutic decision. HighlightsO_LIA novel stratification of glioblastoma based on mass spectrometry was established. C_LIO_LIThree groups with different molecular features and survival were identified. C_LIO_LIThis new classification could improve prognostication and may help therapeutic options. C_LIO_LI8 prognosis markers for oncologist therapeutic decision have been validated. C_LI

cancer biology↗

bioRxiv2020

Preneoplastic lesions fimbria pan-proteomic studies establish the fimbriectomy benefit for BRCA1/2 patients and identify early diagnosis markers of HGSC

Ovarian cancer is the leading cause of death from gynecologic cancer worldwide; however, the origin of ovarian tumors, particularly for high-grade serous carcinoma (HGSC), is still debated. Accumulated evidence converges towards the involvement of the preneoplastic lesions observed in the fimbriated end of the fallopian tubes. In this study, we propose to carry out an in-depth proteomics analysis of these epithelial lesions (p53 signature, serous tubal intraepithelial carcinoma-STIC and serous tubal intraepithelial lesions-STIL) based on spatially resolved proteomic guided by IHC technique. We identified specific clusters related to each preneoplastic lesions, specific protein mutations based on Cosmic database and a Ghost proteome translated from non-coding RNAs and alternative ORFs, using the OpenProt database. Protein networks have been constructed from each cluster utilizing systems biology platform. Generated data were used to confirm the potentially dormant character of the STIL lesion and the more aggressive profile of the STIC which appears closer to HGSC than other lesions. In summary, our results established the chronological mechanisms and genesis of different ovarian cancer phenotypes but also identified the early diagnostic markers of HCSC guiding an adapted therapy and a better patient care.

cancer biology↗

bioRxiv2020

Towards CNN Representations for Small Mass Spectrometry Data Classification: From Transfer Learning to Cumulative Learning

Rapid and accurate clinical diagnosis of pathological conditions remains highly challenging. A very important component of diagnosis tool development is the design of effective classification models with Mass spectrometry (MS) data. Some popular Machine Learning (ML) approaches have been investigated for this purpose but these ML models require time-consuming preprocessing steps such as baseline correction, denoising, and spectrum alignment to remove non-sample-related data artifacts. They also depend on the tedious extraction of handcrafted features, making them unsuitable for rapid analysis. Convolutional Neural Networks (CNNs) have been found to perform well under such circumstances since they can learn efficient representations from raw data without the need for costly preprocessing. However, their effectiveness drastically decreases when the number of available training samples is small, which is a common situation in medical applications. Transfer learning strategies extend an accurate representation model learnt usually on a large dataset containing many categories, to a smaller dataset with far fewer categories. In this study, we first investigate transfer learning on a 1D-CNN we have designed to classify MS data, then we develop a new representation learning method when transfer learning is not powerful enough, as in cases of low-resolution or data heterogeneity. What we propose is to train the same model through several classification tasks over various small datasets in order to accumulate generic knowledge of what MS data are, in the resulting representation. By using rat brain data as the initial training dataset, a representation learning approach can have a classification accuracy exceeding 98% for canine sarcoma cancer cells, human ovarian cancer serums, and pathogenic microorganism biotypes in 1D clinical datasets. We show for the first time the use of cumulative representation learning using datasets generated in different biological contexts, on different organisms, in different mass ranges, with different MS ionization sources, and acquired by different instruments at different resolutions. Our approach thus proposes a promising strategy for improving MS data classification accuracy when only small numbers of samples are available as a prospective cohort. The principles demonstrated in this work could even be beneficial to other domains (astronomy, archaeology...) where training samples are scarce.

bioinformatics↗