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Silvio Bicciato

Publications and source records attributed to Silvio Bicciato.

8 recordsLinked to original sources
PubMed2026

FIERCE: reconstructing dynamic trajectories from the differentiation potency of single cells.

MOTIVATION: Since the introduction of single-cell RNA sequencing (scRNA-seq), numerous computational approaches have been developed to reconstruct dynamic cellular processes from static transcriptional profiles. These methods order cells along continuous trajectories by assessing their similarity in the gene-expression space. However, they rely on several assumptions, such as prior knowledge of the structure and directionality of the expected genealogy. These assumptions can limit their application to complex cellular systems with poorly understood developmental paths. RESULTS: To address this challenge, we introduce FIERCE (Framework for InfERence of the veloCity of Entropy), a novel computational pipeline designed to predict the changes in the differentiation potency of single cells during dynamic processes. Through a fully unsupervised approach, FIERCE enables the inference of cell lineages directly on the differentiation landscape of the biological system, thus eliminating the need for prior specification of developmental parameters. We demonstrate the efficacy of FIERCE by reconstructing three well-known mouse differentiation systems and by quantifying its accuracy on simulated data. AVAILABILITY AND IMPLEMENTATION: The FIERCE R package is available on GitHub at https://github.com/bicciatolab/FIERCE.

Cell Differentiation↗
PubMed2004

Acute leukemia subclassification: a marker protein expression perspective.

Improved leukemia classification and tailoring of therapy have greatly improved patient outcome particularly for children with acute leukemia (AL). Using immunophenotyping, molecular genetics and cytogenetics the low hanging fruits of biomedical research have been successfully incorporated in routine diagnosis of leukemia subclasses. Future improvements in the classification and understanding of leukemia biology will very likely be more slow and laborious. Recently, gene expression profiling has provided a framework for the global molecular analysis of hematological cancers, and high throughput proteomic analysis of leukemia samples is on the way. Here we consider classification of acute leukemia samples by flow cytometry using the marker proteins of immunophenotyping as a component of the proteome. Marker protein expressions are converted into quantitative expression values and subjected to computational analysis. Quantitative multivariate analysis from panels of marker proteins has demonstrated that marker protein expression profiles can distinguish MLLre from non-MLLre ALL cases and also allow to specifically distinguish MLL/AF4 cases. Potentially, these quantitative expression analyses can be used in clinical diagnosis. Immunophenotypic data collection using flow cytometry is a fast and relatively easily accessible technology that has already been implemented in most centers for leukemia diagnosis and the translation into quantitative expression data sets is a matter of flow cytometer settings and output calibration. However, before application in clinical diagnostics can occur it is crucial that quantitative immunophenotypic data set analysis is validated in independent experiments and in large data sets.

Acute Disease↗
PubMed2004

Artificial neural network technologies to identify biomarkers for therapeutic intervention.

High-throughput technologies such as DNA/RNA microarrays, mass spectrometry and protein chips are creating unprecedented opportunities to accelerate towards the understanding of living systems and the identification of target genes and pathways for drug development and therapeutic intervention. However, the increasing volumes of data generated by molecular profiling experiments pose formidable challenges to investigate an overwhelming mass of information and turn it into predictive, deployable markers. Advanced biostatistics and machine learning methods from computer science have been applied to analyze and correlate numerical values of profiling intensities to physiological states. This article reviews the application of artificial neural networks, an information-processing tool, to the identification of sets of diagnostic/prognostic biomarkers from high-throughput profiling data.

Animals↗
PubMed2003

Pattern identification and classification in gene expression data using an autoassociative neural network model.

The application of DNA microarray technology for analysis of gene expression creates enormous opportunities to accelerate the pace in understanding living systems and identification of target genes and pathways for drug development and therapeutic intervention. Parallel monitoring of the expression profiles of thousands of genes seems particularly promising for a deeper understanding of cancer biology and the identification of molecular signatures supporting the histological classification schemes of neoplastic specimens. However, the increasing volume of data generated by microarray experiments poses the challenge of developing equally efficient methods and analysis procedures to extract, interpret, and upgrade the information content of these databases. Herein, a computational procedure for pattern identification, feature extraction, and classification of gene expression data through the analysis of an autoassociative neural network model is described. The identified patterns and features contain critical information about gene-phenotype relationships observed during changes in cell physiology. They represent a rational and dimensionally reduced base for understanding the basic biology of the onset of diseases, defining targets of therapeutic intervention, and developing diagnostic tools for the identification and classification of pathological states. The proposed method has been tested on two different microarray datasets-Golub's analysis of acute human leukemia [Golub et al. (1999) Science 286:531-537], and the human colon adenocarcinoma study presented by Alon et al. [1999; Proc Natl Acad Sci USA 97:10101-10106]. The analysis of the neural network internal structure allows the identification of specific phenotype markers and the extraction of peculiar associations among genes and physiological states. At the same time, the neural network outputs provide assignment to multiple classes, such as different pathological conditions or tissue samples, for previously unseen instances.

Adenocarcinoma↗
PubMed2003

Plasminogen activator inhibitor-2: a molecular biomarker for head and neck cancer progression.

Head and neck squamous cell carcinoma (HNSCC) is an aggressive epithelial malignancy in which the early diagnosis of premalignant lesions is known to directly correlate with increased survival. However, only a portion of biopsies showing dysplasia will progress to cancer, and there are no currently accepted criteria for predicting which lesions will progress. Therefore, diagnostic protocols that can identify the lesions that are likely to become HNSCC are required. RNA was isolated from normal keratinocytes, the immortalized but nontumorigenic HaCat cell line, and the tumor cell lines SCC-4, SCC-9, SCC-25, and OSCC-3. The RNA was then labeled and used to probe nylon microarray filters that contained a total of 9184 genes (5295 named and 3889 Expression Sequence Tags). Genes whose expression demonstrated a 3-fold or greater change were considered significant. Comparison of expression profiles from normal, HaCat, and four tumor cell lines revealed changes in gene expression in a total of 508 genes. Of these, 16 genes showed a consistent loss of expression when comparing normal to immortalized keratinocytes. In addition, 10 genes demonstrated a consistent loss of expression in the tumor cell lines only. In this latter group of genes, plasminogen activator inhibitor-2 (PAI-2), a gene whose expression has been linked to cell invasion, was additionally investigated. Altered expression of PAI-2 in the different cultured cells was validated via real-time quantitative-PCR. In addition, immunohistochemical evaluation of biopsy samples revealed a high expression of PAI-2 in both normal and dysplastic epithelium with a marked decrease of expression in areas of the biopsies containing HNSCC. These data demonstrate that genomic profiling can then be used to identify potential genotypic/phenotypic biomarkers that may predict which dysplastic lesions are most likely to progress to HNSCC.

Biomarkers, Tumor↗
PubMed2002

Prediction of in vivo synergistic activity of antiangiogenic compounds by gene expression profiling.

Angiogenesis, an essential phenotype for tumor formation, requires the interaction of many cells within the tumor microenvironment. Therefore, successful antiangiogenic therapies must be able to block all of the different mechanisms tumors use to induce neovascularization. A major challenge for developing such protocols is determining which agents are likely to have the highest degree of synergistic activity in vivo. We treated human microvascular endothelial cells with six inhibitors of angiogenesis and used microarrays to seek divergent patterns of gene expression suggestive of potential synergies. The expression profiles of a thrombospondin-mimetic peptide (DI-TSPa) and TNP-470 (TNP) were very similar, whereas endostatin had a dramatically different profile. In vitro, endostatin was synergistically antiangiogenic with either TNP-470 or DI-TSPa. In vivo, mice bearing Lewis lung carcinoma cells treated with a combination of endostatin and either DI-TSPa or TNP-470, at doses that were ineffective when used alone, resulted in a marked inhibition of tumor growth and decreased tumor angiogenesis. Conversely, animals treated with both DI-TSPa and TNP-470 demonstrated a modest effect on both tumor growth and angiogenesis. These results suggest that even in the absence of a complete mechanistic understanding of how these inhibitors work, gene expression profiling may be used to predict synergistic antiangiogenic activity and thus maximize their antitumor efficacy.

Angiogenesis Inhibitors↗
PubMedDate not supplied

Fermentation diagnosis by multivariate statistical analysis.

During the course of fermentation, online measuring procedures able to estimate the performance of the current operation are highly desired. Unfortunately, the poor mechanistic understanding of most biologic systems hampers attempts at direct online evaluation of the bioprocess, which is further complicated by the lack of appropriate online sensors and the long lag time associated with offline assays. Quite often available data lack sufficient detail to be directly used, and after a cursory evaluation are stored away. However, these historic databases of process measurements may still retain some useful information. A multivariate statistical procedure has been applied for analyzing the measurement profiles acquired during the monitoring of several fed-batch fermentations for the production of erythromycin. Multivariate principal component analysis has been used to extract information from the multivariate historic database by projecting the process variables onto a low-dimensional space defined by the principal components. Thus, each fermentation is identified by a temporal profile in the principal component plane. The projections represent monitoring charts, consistent with the concept of statistical process control, which are useful for tracking the progress of each fermentation batch and identifying anomalous behaviors (process diagnosis and fault detection).

Biotechnology↗