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Microplastic (MP) pollution is increasingly acknowledged as a critical environmental and public health issue. This study sought to establish a robust, clinically compatible method for detecting MP particles in deparaffinized formalin-fixed paraffin-embedded (FFPE) human colon tissue sections, using protocols compatible with routine clinical pathology. We employed mid-infrared photothermal (MIP) microscopy—also referred to as optical photothermal infrared (OPTIR) spectroscopy—as a non-destructive, high-resolution technique for chemical characterization and spatial mapping of polymer particles in intact FFPE samples. Following OPTIR analysis, identical sections underwent hematoxylin and eosin (H&E) staining to facilitate precise histopathological evaluation in defined regions of interest. Using this integrated workflow, we detected and localized polyethylene (PE), polystyrene (PS), and polyethylene terephthalate (PET) particles (21 PE particles, 1 PS particle, and 1 PET fiber) within distinct tissue areas. Subsequent histological assessment revealed characteristic inflammatory features near to these identified MP particles. To our knowledge, this represents the first demonstration of a diagnostic workflow that enables combined infrared spectroscopic and histopathological analysis of MPs in routinely processed human FFPE tissue. This approach offers a promising avenue to elucidate the role of microplastic accumulation in human disease and supports further investigation into potential mechanistic links between MP exposure and inflammatory processes in the colon.

High‐throughput and streamlined workflows are essential in clinical proteomics for standardized processing of samples from a variety of sources, including fresh‐frozen tissue, FFPE tissue, or blood. To reach this goal, we have implemented single‐pot solid‐phase‐enhanced sample preparation (SP3) on a liquid handling robot for automated processing (autoSP3) of tissue lysates in a 96‐well format. AutoSP3 performs unbiased protein purification and digestion, and delivers peptides that can be directly analyzed by LCMS, thereby significantly reducing hands‐on time, reducing variability in protein quantification, and improving longitudinal reproducibility. We demonstrate the distinguishing ability of autoSP3 to process low‐input samples, reproducibly quantifying 500–1,000 proteins from 100 to 1,000 cells. Furthermore, we applied this approach to a cohort of clinical FFPE pulmonary adenocarcinoma (ADC) samples and recapitulated their separation into known histological growth patterns. Finally, we integrated autoSP3 with AFA ultrasonication for the automated end‐to‐end sample preparation and LCMS analysis of 96 intact tissue samples. Collectively, this constitutes a generic, scalable, and cost‐effective workflow with minimal manual intervention, enabling reproducible tissue proteomics in a broad range of clinical and non‐clinical applications. Synopsis The study presents an automated sample preparation pipeline for low‐input proteomics based on the SP3 method. The seamless integration of tissue lysis with autoSP3 in a 96‐well format features low variability, high sensitivity and longitudinal reproducibility for clinical studies. An automated, scalable, and cost‐effective workflow (autoSP3) allows reproducible tissue proteomics in a broad range of clinical and non‐clinical applications. Automated tissue lysis is integrated with autoSP3 for an end‐to‐end workflow with minimal manual interference. The workflow allows reduced variability in protein quantification and increased longitudinal reproducibility. Minimal sample losses facilitate low‐input applications in a standardized workflow. Graphical Abstract The study presents an automated sample preparation pipeline for low‐input proteomics based on the SP3 method. The seamless integration of tissue lysis with autoSP3 in a 96‐well format features low variability, high sensitivity and longitudinal reproducibility for clinical studies.

Single-cell RNA sequencing (scRNA-seq) provides a comprehensive understanding of cellular complexity; however, its requirement for fresh or frozen samples limits its flexibility. To overcome this limitation to effectively leverage clinical samples, Chromium Fixed RNA Profiling on formalin-fixed paraffin-embedded (FFPE) tissue blocks (scFFPE-seq) was developed to perform single-nucleus RNA sequencing from nuclei isolated from FFPE. In this study, we utilized fresh tissue samples from colon, ileum, and skin to assess the viability of scFFPE-seq compared to these fresh samples. We were able to recover unique cell types from challenging FFPE tissues and validated scFFPE-seq findings through Hematoxylin and Eosin (H&E) images. The results demonstrated that scFFPE-seq effectively captured the single-cell transcriptome in FFPE tissues, obtaining comparable cell abundance, cell type annotation, and pathway characterization to those in fresh tissues. Overall, the study presents strong evidence of the potential of scFFPE-seq to enhance scientific knowledge by enabling the generation of high-quality, sensitive single-nucleus RNA-seq data from preserved tissue samples. This technique unlocks the vast archives of FFPE samples for extensive retrospective genomic studies.

In recent years, efficient modeling and forecasting of electricity prices became highly important for all the market participants for developing bidding strategies and making investment decisions. However, as electricity prices exhibit specific features, such as periods of high volatility, seasonal patterns, calendar effects, nonlinearity, etc., their accurate forecasting is challenging. This study proposes a functional forecasting method for the accurate forecasting of electricity prices. A functional autoregressive model of order P is suggested for short-term price forecasting in the electricity markets. The applicability of the model is improved with the help of functional final prediction error (FFPE), through which the model dimensionality and lag structure were selected automatically. An application of the suggested algorithm was evaluated on the Italian electricity market (IPEX). The out-of-sample forecasted results indicate that the proposed method performs relatively better than the nonfunctional forecasting techniques such as autoregressive (AR) and naïve models.

Formalin‐fixed, paraffin‐embedded (FFPE), biobanked tissue samples offer an invaluable resource for clinical and biomarker research. Here, we developed a pressure cycling technology (PCT)‐SWATH mass spectrometry workflow to analyze FFPE tissue proteomes and applied it to the stratification of prostate cancer (PCa) and diffuse large B‐cell lymphoma (DLBCL) samples. We show that the proteome patterns of FFPE PCa tissue samples and their analogous fresh‐frozen (FF) counterparts have a high degree of similarity and we confirmed multiple proteins consistently regulated in PCa tissues in an independent sample cohort. We further demonstrate temporal stability of proteome patterns from FFPE samples that were stored between 1 and 15 years in a biobank and show a high degree of the proteome pattern similarity between two types of histological regions in small FFPE samples, that is, punched tissue biopsies and thin tissue sections of micrometer thickness, despite the existence of a certain degree of biological variations. Applying the method to two independent DLBCL cohorts, we identified myeloperoxidase, a peroxidase enzyme, as a novel prognostic marker. In summary, this study presents a robust proteomic method to analyze bulk and biopsy FFPE tissues and reports the first systematic comparison of proteome maps generated from FFPE and FF samples. Our data demonstrate the practicality and superiority of FFPE over FF samples for proteome in biomarker discovery. Promising biomarker candidates for PCa and DLBCL have been discovered. Here, we report a pressure cycling technology‐SWATH data‐independent acquisition mass spectrometry workflow to reproducibly analyze punched and sectioned formalin‐fixed, paraffin‐embedded (FFPE) tissue proteomes in high‐throughput fashion. A FFPE tissue biobank can be thereby converted into a digital biobank of comprehensive tissue proteome archives which could be mined perpetually for clinical and biomarker research.

Purpose Fresh-frozen (FF) tissue is the optimal source of DNA for whole-genome sequencing (WGS) of cancer patients. However, it is not always available, limiting the widespread application of WGS in clinical practice. We explored the viability of using formalin-fixed, paraffin-embedded (FFPE) tissues, available routinely for cancer patients, as a source of DNA for clinical WGS. Methods We conducted a prospective study using DNAs from matched FF, FFPE, and peripheral blood germ-line specimens collected from 52 cancer patients (156 samples) following routine diagnostic protocols. We compared somatic variants detected in FFPE and matching FF samples. Results We found the single-nucleotide variant agreement reached 71% across the genome and somatic copy-number alterations (CNAs) detection from FFPE samples was suboptimal (0.44 median correlation with FF) due to nonuniform coverage. CNA detection was improved significantly with lower reverse crosslinking temperature in FFPE DNA extraction (80 °C or 65 °C depending on the methods). Our final data showed somatic variant detection from FFPE for clinical decision making is possible. We detected 98% of clinically actionable variants (including 30/31 CNAs). Conclusion We present the first prospective WGS study of cancer patients using FFPE specimens collected in a routine clinical environment proving WGS can be applied in the clinic.

Uterine leiomyomas are benign smooth muscle tumors occurring in 70% of women of reproductive age. The majority of leiomyomas harbor one of three well-established genetic changes: a hotspot mutation in , overexpression of , or biallelic loss of . The majority of studies have classified leiomyomas by complex and costly methods, such as whole-genome sequencing, or by combining multiple traditional methods, such as immunohistochemistry and Sanger sequencing. The type of specimens and the amount of resources available often determine the choice. A more universal, cost-effective, and scalable method for classifying leiomyomas is needed. The aim of this study was to evaluate whether RNA sequencing can accurately classify formalin-fixed paraffin-embedded (FFPE) leiomyomas. We performed 3'RNA sequencing with 44 leiomyoma and 5 myometrium FFPE samples, revealing that the samples clustered according to the mutation status of , , and . Furthermore, we confirmed each subtype in a publicly available fresh frozen dataset. These results indicate that a targeted 3'RNA sequencing panel could serve as a cost-effective and robust tool for stratifying both fresh frozen and FFPE leiomyomas. This study also highlights 3'RNA sequencing as a promising method for studying the abundance of unexploited tissue material that is routinely stored in hospital archives.

Objective The performance of FFPE tissue-derived DNA on the MethylationEpicV1.0 array can be unpredictable as the protocol only has two quality checks; checkpoint 1 for DNA quantity and checkpoint 2 for DNA quality assessment. We sought to incorporate a third, previously developed bisulfite conversion quality check prior to processing 255 FFPE tissue derived DNA samples. Results FFPE tissue-derived DNAs were prepared for 255 prostate tumour specimens and four controls. Checkpoint 1 assessed all samples to have 500ng DNA available for analysis except for two samples that yielded 483 and 486ng. All DNA samples passed both the quality checkpoint 2 and the bisulfite conversion quality assessment checkpoint 3. Assessment of array performance showed one of the 259 (0.4%) DNA samples had less than 90% of probes detected at p  = 0.05. Controls and replicate showed reliable reproducibility with correlations of 0.981 and 0.994. High quantity and quality measures of the FFPE tissue-derived DNAs (assessed by checkpoint 1 and 2) were likely responsible for 99.6% of DNAs producing high quality EPIC array data. This report suggests that checkpoint 3 has limited value in a setting where the FFPE tissue derived DNA has high quantity and quality measures.

Abstract Background Conventional HER2-targeting therapies improve outcomes for patients with HER2-positive breast cancer (BC), defined as tumors showing HER2 protein overexpression by immunohistochemistry and/or ERBB2 gene amplification determined by in situ hybridization (ISH). Emerging HER2-targeting compounds show benefit in some patients with neither HER2 protein overexpression nor ERBB2 gene amplification, creating a need for new assays to select HER2-low tumors for treatment with these compounds. We evaluated the analytical performance of a targeted mass spectrometry-based assay for quantifying HER2 protein in formalin-fixed paraffin-embedded (FFPE) and frozen BC biopsies. Methods We used immunoaffinity-enrichment coupled to multiple reaction monitoring-mass spectrometry (immuno-MRM-MS) to quantify HER2 protein (as peptide GLQSLPTHDPSPLQR) in 96 frozen and 119 FFPE BC biopsies. We characterized linearity, lower limit of quantification (LLOQ), and intra- and inter-day variation of the assay in frozen and FFPE tissue matrices. We determined concordance between HER2 immuno-MRM-MS and predicate immunohistochemistry and ISH assays and examined the benefit of multiplexing the assay to include proteins expressed in tumor subcompartments (e.g., stroma, adipose, lymphocytes, epithelium) to account for tissue heterogeneity. Results HER2 immuno-MRM-MS assay linearity was ≥103, assay coefficient of variation was 7.8% (FFPE) and 5.9% (frozen) for spiked-in analyte, and 7.7% (FFPE) and 7.9% (frozen) for endogenous measurements. Immuno-MRM-MS-based HER2 measurements strongly correlated with predicate assay HER2 determinations, and concordance was improved by normalizing to glyceraldehyde-3-phosphate dehydrogenase. HER2 was quantified above the LLOQ in all tumors. Conclusions Immuno-MRM-MS can be used to quantify HER2 in FFPE and frozen BC biopsies, even at low HER2 expression levels.

Background RNA sequencing (RNA-Seq) is often used for transcriptome profiling as well as the identification of novel transcripts and alternative splicing events. Typically, RNA-Seq libraries are prepared from total RNA using poly(A) enrichment of the mRNA (mRNA-Seq) to remove ribosomal RNA (rRNA), however, this method fails to capture non-poly(A) transcripts or partially degraded mRNAs. Hence, a mRNA-Seq protocol will not be compatible for use with RNAs coming from Formalin-Fixed and Paraffin-Embedded (FFPE) samples. Results To address the desire to perform RNA-Seq on FFPE materials, we evaluated two different library preparation protocols that could be compatible for use with small RNA fragments. We obtained paired Fresh Frozen (FF) and FFPE RNAs from multiple tumors and subjected these to different gene expression profiling methods. We tested 11 human breast tumor samples using: (a) FF RNAs by microarray, mRNA-Seq, Ribo-Zero-Seq and DSN-Seq (Duplex-Specific Nuclease) and (b) FFPE RNAs by Ribo-Zero-Seq and DSN-Seq. We also performed these different RNA-Seq protocols using 10 TCGA tumors as a validation set. The data from paired RNA samples showed high concordance in transcript quantification across all protocols and between FF and FFPE RNAs. In both FF and FFPE, Ribo-Zero-Seq removed rRNA with comparable efficiency as mRNA-Seq, and it provided an equivalent or less biased coverage on gene 3′ ends. Compared to mRNA-Seq where 69% of bases were mapped to the transcriptome, DSN-Seq and Ribo-Zero-Seq contained significantly fewer reads mapping to the transcriptome (20-30%); in these RNA-Seq protocols, many if not most reads mapped to intronic regions. Approximately 14 million reads in mRNA-Seq and 45–65 million reads in Ribo-Zero-Seq or DSN-Seq were required to achieve the same gene detection levels as a standard Agilent DNA microarray. Conclusions Our results demonstrate that compared to mRNA-Seq and microarrays, Ribo-Zero-Seq provides equivalent rRNA removal efficiency, coverage uniformity, genome-based mapped reads, and consistently high quality quantification of transcripts. Moreover, Ribo-Zero-Seq and DSN-Seq have consistent transcript quantification using FFPE RNAs, suggesting that RNA-Seq can be used with FFPE-derived RNAs for gene expression profiling.