application of multivariate data analysis for the classification of two dimensional gel images in neuroproteomics

Two-dimensional gel electrophoresis (2DE) still plays a key role in proteomics for exploring the protein content of complex biological mixtures. However, the development of fully automatic strategies in extracting interpretable information from gel images is still a challenging task. In this work, we present a computational strategy aiming at an automatic classification of the discriminant patterns emerging from separation images intended as fingerprints of the correspondent biological conditions. The method was applied to gel images acquired in a study on motor neuron diseases: 33 2DE maps generated from samples of cerebrospinal fluid were processed (26 pathologic and 7 control subjects). Quantitative image descriptors were extracted and fitted to a partial least squares-discriminant analysis (PLSDA) assessing the chance to classify the samples. Moreover, the model was able to identify gel areas that most differ through the clinical categories. Combining multivariate statistical techniques with 2DE may represent a valid tool to extract informative protein patterns. This kind of approach can contribute to the development of a system of screening to discriminate different clinical conditions on the basis of the overall patterns emerging from the maps, representing a useful complementary analysis in the routine of a proteomic laboratory

Application of multivariate data analysis for the classification of two dimensional gel images in Neuroproteomics

Two-dimensional gel electrophoresis (2DE) still plays a key role in proteomics for exploring the protein content of complex biological mixtures. However, the development of fully automatic strategies in extracting interpretable information from gel images is still a challenging task. In this work, we present a computational strategy aiming at an automatic classification of the discriminant patterns emerging from separation images intended as fingerprints of the correspondent biological conditions. The method was applied to gel images acquired in a study on motor neuron diseases: 33 2DE maps generated from samples of cerebrospinal fluid were processed (26 pathologic and 7 control subjects). Quantitative image descriptors were extracted and fitted to a partial least squares-discriminant analysis (PLSDA) assessing the chance to classify the samples. Moreover, the model was able to identify gel areas that most differ through the clinical categories. Combining multivariate statistical techniques with 2DEs may represent a valid tool to extract informative protein patterns. This kind of approach can contribute to the development of a system of screening to discriminate different clinical conditions on the basis of the overall patterns emerging from the maps, representing a useful complementary analysis in the routine of a proteomic laboratory. © 2011 Mazzara S, et al

A multivariate analysis of protein microarrays for signature selection profiles

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A Spatially Resolved Dark- versus Light-Zone Microenvironment Signature Subdivides Germinal Center-Related Aggressive B Cell Lymphomas

We applied digital spatial profiling for 87 immune and stromal genes to lymph node germinal center (GC) dark- and light-zone (DZ/LZ) regions of interest to obtain a differential signature of these two distinct microenvironments. The spatially resolved 53-genes signature, comprising key genes of the DZmutational machinery and LZ immune and mesenchymal milieu, was applied to the transcriptomes of 543 GC-related diffuse large B cell lymphomas and double-hit ( DH) lymphomas. According to the DZ/LZ signature, the GC-related lymphomas were sub-classified into two clusters. The subgroups differed in the distribution of DH cases and survival, with most DH displaying a distinct DZ-like profile. The clustering analysis was also performed using a 25-genes signature composed of genes positively enriched in the non-B, stromal sub-compartments, for the first time achieving DZ/LZ discrimination based on stromal/immune features. The report offers new insight into the GC microenvironment, hinting at a DZ microenvironment of origin in DH lymphomas

The identification of TCF1+ progenitor exhausted T cells in THRLBCL may predict a better response to PD-1/PD-L1 blockade

T-cell/histiocyte-rich large B-cell lymphoma (THRLBCL) is a rare and aggressive variant of diffuse large B-cell lymphoma (DLBCL) that usually affects young to middle-aged patients, with disseminated disease at presentation. The tumor microenvironment (TME) plays a key role in THRLBCL due to its peculiar cellular composition (< 10% neoplastic B cells interspersed in a cytotoxic T-cell/histiocyte-rich background). A significant percentage of THRLBCL is refractory to rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (RCHOP)-based regimens and to chimeric antigen receptor T-cell therapy; thus, the development of a specific therapeutic approach for these patients represents an unmet clinical need. To better understand the interaction of immune cells in THRLBCL TME and identify more promising therapeutic strategies, we compared the immune gene expression profiles of 12 THRLBCL and 10 DLBCL samples, and further corroborated our findings in an extended in silico set. Gene coexpression network analysis identified the predominant role of the programmed cell death protein 1 (PD-1)/programmed cell death ligand 1 (PD-L1) axis in the modulation of the immune response. Furthermore, the PD-1/PD-L1 activation was flanked by the overexpression of 48 genes related to the functional exhaustion of T cells. Globally, THRLBCL TME was highly interferon-inflamed and severely exhausted. The immune gene profiling findings strongly suggest that THRLBCL may be responsive to anti-PD-1 therapy but also allowed us to take a step forward in understanding THRLBCL TME. Of therapeutic relevance, we validated our results by immunohistochemistry, identifying a subset of TCF1(+) (T cell-specific transcription factor 1, encoded by the TCF7 gene) progenitor exhausted T cells enriched in patients with THRLBCL. This subset of TCF1(+) exhausted T cells correlates with good clinical response to immune checkpoint therapy and may improve prediction of anti-PD-1 response in patients with THRLBCL

Extracting Discriminant Patterns in Two-Dimensional Gel Images:An Application to Neuroproteomics

The proteomic approach may be extremely useful when searching for the causes of variations in the protein content of cells and of biological fluids associated with the development of various diseases such as neurodegenerative disorders. Today, two-dimensional gel electrophoresis (2DE) is still widely used as method of choice in proteomics for its ability to analyze many proteins simultaneously yielding a global view of protein expression. However, the automatic extraction of information from gel images is still a challenging task. In this paper, we propose a computational strategy to the aim of identifying patterns that are representative of a clinical status. The method was applied to an experimental protocol including two different clinical groups of amyotrophic lateral sclerosis (ALS) and peripheral neuropathy patients: 32 2DE maps generated from cerebrospinal fluid (24 pathological and 8 control subjects) were processed. Quantitative image descriptors were extracted to describe each image and dealt with the dimension reduction technique of local tangent space alignment (LTSA). The discovered low-dimensional structure reveals clear discrimination between diseased and control subjects, showing the informativeness of the adopted descriptors and providing the bases for classification of this kind of samples.BioMed@POLIMI Proc 1st Workshop on the Life Sciences at Politecnico di Milano, Milano, Nov. 201

AIH sera recognize UNQ9419 and CHAD.

<p>(A), SDS-PAGE (left panel) and western blot against anti-His antibody analysis (right panel) of the purified UNQ9419 and CHAD recombinant proteins. (B), western blot analysis of the purified UNQ9419 and CHAD recombinant proteins against sera from AIH patients (left panel) and no AIH subjects (right panel), respectively.</p

Selection strategy of serum autoantigens.

<p>The Venn diagram shows the autoantigen selection obtained according to (i) VIP scores >1.0 and (ii) a delta difference recognition frequency of 25%. 70 autoantigens overlap between the two filter criteria. Then, final selection of autoantigens was based on relative frequency of autoantigens from all generated datasets. There were 31 variables that were present in all datasets.</p

Overview of the PLS-DA analysis for the comparison between AIH and HD group.

<p>The normalized Mean Fluorescence Intensity (MFI) from microarray data was analyzed using PCA and PLS-DA models. (A) PCA shows that AIH (red circle) and HD (green triangle) have distinctive profiles with little overlap between the two groups of samples; the exception was the sample HD0088 (blue arrow) so this sample was omitted from the subsequently explorative analysis. (B) Plot of R²Y (explained variation) and Q²Y (predicted variation) shows how the considered parameters change as a function of increasing model complexity. Three components were calculated through cross-validation, R²Y and Q²Y were 74.28% and 62.18% and resulted significant in order to explain the relationship between the descriptor matrix and the class response. (C) PLS-DA 3D score plot reveals that each sample is found close to the samples belonging to the same subgroup. Samples are coloured according to the disease status (AIH- red circle, HD green triangle—the axes of the plot indicate PLS-DA component 1–3). (D) Density plot of the Q²Y values in the analysis of 1000 permutation tests, solid red line shows the real Q²Y value. Such reference distribution can be seen as sign of the degrees of overfit and overprediction of the model. The permutation test showed that the real PLS-DA model was not over-fitted and not over-predicted.</p

Evaluation of feature stability.

<p>Robustness of the R-SVM and PLS-DA rankers across the different 50 datasets is plotted as heat maps. Columns and rows represent the independent 50 subsets and each square indicates the Tanimoto index between two subsets. The colour code of the heat map ranges from blue to red where a blue colour reflects a low similarity index suggesting few proteins in common between the subsets while a red colour denotes an high similarity index. For each considered selection method a similarity heat map is obtained. (A), The average similarity over all pair wise comparison is 69% for PLS-DA; (B), and 31% for R-SVM; thus PLS-DA outperforms the R-SVM.</p