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Morphological profiling is a common approach to investigate the modes of action (MOAs) of compounds. Most methods rely on fixed-cell assays, which provide only a single snapshot at a predefined time point and overlook the dynamic nature of cellular responses. In contrast, live-cell imaging tracks responses over time, offering deeper insight into compound-specific effects and mechanisms; however, time-series analysis of image data remains challenging due to limited analytical tools. We present Live Cell Temporal Profiling (LCTP), a workflow for morphological profiling of label-free live-cell time series data that yields interpretable, biologically relevant results. We showcase LCTP in an MOA classification study using label-free data. The workflow integrates established deep-learning components, cell segmentation, live/dead classification, and single-cell feature extraction, with data-driven models to capture MOA-specific temporal phenotypes and produce time-resolved profiles that can be compared across compounds and cell lines. We assess MOA classification performance using double-blinded cross-validation simulating a real-world screening scenario. LCTP significantly improves MOA classification over single–time point analysis, consistently across both cell lines used in the study. Time-resolved phenotypic modelling reveals transient, sustained, and delayed responses, clarifying compound-specific temporal effects and mechanisms across MOAs. The presented workflow is modular: each step removes irrelevant information, enriching signal, and enabling straightforward updates as technologies evolve and as new technologies become available, while supporting reuse across studies broadly. We believe LCTP adds substantial value to high-throughput compound screening, showing that live-cell imaging combined with this workflow yields informative visualizations of temporal effects and improved MOA classification.

Cell culture bioprocess data are typically collected across many timepoints and batches, where numerous analytes covary with each other and, critically, with elapsed process time. This time dependence can inflate performance metrics and compromise the validity of multivariate models. We introduce time-adjusted performance evaluation (TAPE), a regression-agnostic validation technique that quantifies and separates time-driven from time-independent predictivity. TAPE pairs leave-one-group-out cross-validation with per-timepoint centering to decompose performance into between-timepoint (time-dependent) and within-timepoint (time-decoupled) parts by comparing predicted and observed deviations from each timepoint mean. Applying TAPE to orthogonal partial least squares models across five Chinese hamster ovary cell culture datasets (three Raman spectroscopy, one metabolomics, and one transcriptomics), several ostensibly strong models’ predictivity was largely explained by timepoint means alone. After removing between-timepoint variation, only models with sample–response relationships independent of time retained good predictivity. For Raman, only models for Raman-active analytes (glucose, lactate) remained predictive, whereas Raman-inactive ones (K+, NH4+) did not. In the omics studies, the models for titer, viable cell density, growth rate, and death rates were predominantly time-driven. By quantifying time’s contribution to model performance, TAPE helps prevent misleadingly good performance metrics and supports more reliable multivariate modeling of time-series bioprocess data.

Multiclass data sets and large-scale studies are increasingly common in omics sciences, drug discovery, and clinical research due to advancements in analytical platforms. Efficiently handling these data sets and discerning subtle differences across multiple classes remains a significant challenge. In metabolomics, two-class orthogonal projection to latent structures discriminant analysis (OPLS-DA) models are widely used due to their strong discrimination capabilities and ability to provide interpretable information on class differences. However, these models face challenges in multiclass settings. A common solution is to transform the multiclass comparison into multiple two-class comparisons, which, while more effective than a global multiclass OPLS-DA model, unfortunately results in a manual, time-consuming model-building process with complicated interpretation. Here, we introduce an extension of OPLS-DA for data-driven multiclass classification: orthogonal partial least squares-hierarchical discriminant analysis (OPLS-HDA). OPLS-HDA integrates hierarchical cluster analysis (HCA) with the OPLS-DA framework to create a decision tree, addressing multiclass classification challenges and providing intuitive visualization of interclass relationships. To avoid overfitting and ensure reliable predictions, we use cross-validation during model building. Benchmark results show that OPLS-HDA performs competitively across diverse data sets compared to eight established methods. This method represents a significant advancement, offering a powerful tool to dissect complex multiclass data sets. With its versatility, interpretability, and ease of use, OPLS-HDA is an efficient approach to multiclass data analysis applicable across various fields.

Biopharmaceuticals are medical compounds derived from biological sources and are often manufactured by living cells, primarily Chinese hamster ovary (CHO) cells. CHO cells display variation among cell clones, leading to growth and productivity differences that influence the product's quantity and quality. The biological and environmental factors behind these differences are not fully understood. To identify metabolites with a consistent relationship to productivity or cell death over time, we analyzed the extracellular metabolome of 11 CHO clones with different growth and productivity characteristics over 14 days. However, in bioreactor processes, metabolic profiles and process variables are both strongly time-dependent, confounding the metabolite-process variable relationship. To address this, we customized an existing hierarchical approach for handling time dependency to highlight metabolites with a consistent correlation to a process variable over a selected timeframe. We benchmarked this new method against conventional orthogonal partial least squares (OPLS) models. Our hierarchical method highlighted several metabolites consistently related to productivity or cell death that the conventional method missed. These metabolites were biologically relevant; most were known already, but some that had not been reported in CHO literature before, such as 3-methoxytyrosine and succinyladenosine, had ties to cell death in studies with other cell types. The metabolites showed an inverse relationship with the response variables: those positively correlated with productivity were typically negatively correlated with the death rate, or vice versa. For both productivity and cell death, the citrate cycle and adjacent pathways (pyruvate, glyoxylate, pantothenate) were among the most important. In summary, we have proposed a new method to analyze time-dependent omics data in bioprocess production. This approach allowed us to identify metabolites tied to cell death and productivity that were not detected with traditional models.

Introduction: Diagnosis and monitoring of metastatic colorectal cancer (mCRC) depend on diagnostic imaging. Circulating carcinoembryonic antigen (CEA) can be analyzed but no optimal, non-invasive biomarker exists. Circulating collagen IV (COL IV) is a promising biomarker in patients with colorectal liver metastases (CLM). This study aimed to evaluate COL IV and other cancer-related and stroma-derived proteins as biomarkers for mCRC.

Materials & methods: Plasma COL IV and 10 other proteins were analyzed with ELISA and Luminex multiplex assays.

Results: mCRC patients have elevated levels of circulating COL IV, CEA, interleukin-8 (IL-8), hepatocyte growth factor (HGF), cytokeratin-19 fragments (CYFRA 21-1), osteopontin (OPN), and migration inhibitory factor (MIF) compared to primary CRC (pCRC) patients. COL IV is elevated in mCRC patients compared to healthy individuals. Levels of COL IV, CEA, OPN, CYFRA 21-1, IL-8, and tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) were dependent on the metastatic site. OPN, CEA, and HGF are very good at discriminating between mCRC patients and pCRC controls. COL IV is very good at distinguishing between mCRC patients and healthy controls. The combination of OPN + CEA is superior at detecting mCRC than CEA alone. High HGF and COL IV levels correlate to poor prognosis.

Conclusion: OPN, CEA, and HGF are potential biomarkers for mCRC. COL IV is a potential biomarker for CLM. The combination of OPN with CEA is superior to CEA alone in detecting mCRC. Levels of circulating proteins depend on metastatic localization, implying that a combination of markers is better than single markers in detecting mCRC disease. High levels of COL IV and HGF have potential prognostic value.

Cell Painting is an established community-based microscopy-assay platform that provides high-throughput, high-content data for biological readouts. In November 2022, the JUMP-Cell Painting Consortium released the largest publicly available Cell Painting dataset with CellProfiler features, comprising more than 2 billion cell images. This dataset is designed for predicting the activity and toxicity of 115k drug compounds, with the aim to make cell images as computable as genomes and transcriptomes. In this context, our paper introduces a scalable and computationally efficient data analytics workflow created to meet the needs of researchers. This data-driven workflow facilitates the comparison of drug treatment effects through significant and biologically relevant insights. The workflow consists of two parts: first, the Equivalence score (Eq. score), a straightforward yet sophisticated metric highlighting relevant deviations from negative controls based on cell image morphology; second, the scalability of the workflow, by utilizing the Eq. scores on a large scale to predict and classify the subtle morphological changes in cell image profiles. By doing so, we show classification improvements compared to using the raw CellProfiler features on the CPJUMP1-pilot dataset on three types of perturbations. We hope that our workflow's contributions will enhance drug screening efficiency and streamline the drug development process. As this process is resource-intensive, every incremental improvement is valuable. Through our collective efforts in advancing the understanding of high-throughput image-based data, we aim to reduce both the time and cost of developing new, life-saving treatments.

Background: Pancreatic ductal adenocarcinoma (pancreatic cancer) is often detected at late stages resulting in poor overall survival. To improve survival, more patients need to be diagnosed early when curative surgery is feasible. We aimed to identify circulating metabolites that could be used as early pancreatic cancer biomarkers.

Methods: We performed metabolomics by liquid and gas chromatography-mass spectrometry in plasma samples from 82 future pancreatic cancer patients and 82 matched healthy controls within the Northern Sweden Health and Disease Study (NSHDS). Logistic regression was used to assess univariate associations between metabolites and pancreatic cancer risk. Least absolute shrinkage and selection operator (LASSO) logistic regression was used to design a metabolite-based risk score. We used receiver operating characteristic (ROC) analyses to assess the discriminative performance of the metabolite-based risk score.

Results: Among twelve risk-associated metabolites with a nominal P value <0.05, we defined a risk score of three metabolites [indoleacetate, 3-hydroxydecanoate (10:0-OH), and retention index (RI): 2,745.4] using LASSO. A logistic regression model containing these three metabolites, age, sex, body mass index (BMI), smoking status, sample date, fasting status, and carbohydrate antigen 19-9 (CA 19-9) yielded an internal area under curve (AUC) of 0.784 [95% confidence interval (CI): 0.714–0.854] compared to 0.681 (95% CI: 0.597–0.764) for a model without these metabolites (P value =0.007). Seventeen metabolites were significantly associated with pancreatic cancer survival [false discovery rate (FDR) <0.1].

Conclusions: Indoleacetate, 3-hydroxydecanoate (10:0-OH), and RI: 2,745.4 were identified as the top candidate biomarkers for early detection. However, continued efforts are warranted to determine the usefulness of these metabolites as early pancreatic cancer biomarkers.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease with poor survival. Novel biomarkers are urgently needed to improve the outcome through early detection. Here, we aimed to discover novel biomarkers for early PDAC detection using multi-omics profiling in pre-diagnostic plasma samples biobanked after routine health examinations.

A nested case-control study within the Northern Sweden Health and Disease Study was designed. Pre-diagnostic plasma samples from 37 future PDAC patients collected within 2.3 years before diagnosis and 37 matched healthy controls were included. We analyzed metabolites using liquid chromatography mass spectrometry and gas chromatography mass spectrometry, microRNAs by HTG edgeseq, proteins by multiplex proximity extension assays, as well as three clinical biomarkers using milliplex technology. Supervised and unsupervised multi-omics integration were performed as well as univariate analyses for the different omics types and clinical biomarkers. Multiple hypothesis testing was corrected using Benjamini-Hochberg's method and a false discovery rate (FDR) below 0.1 was considered statistically significant.

Carbohydrate antigen (CA) 19-9 was associated with PDAC risk (OR [95 % CI] = 3.09 [1.31–7.29], FDR = 0.03) and increased closer to PDAC diagnosis. Supervised multi-omics models resulted in poor discrimination between future PDAC cases and healthy controls with obtained accuracies between 0.429–0.500. No single metabolite, microRNA, or protein was differentially altered (FDR < 0.1) between future PDAC cases and healthy controls.

CA 19-9 levels increase up to two years prior to PDAC diagnosis but extensive multi-omics analysis including metabolomics, microRNAomics and proteomics in this cohort did not identify novel early biomarkers for PDAC.

Purpose: Peritoneal infection, due to anastomotic leakage, after resection for colorectal cancer have been shown to associate with increased cancer recurrence and mortality, as well as cardiovascsular morbidity. Alterations in circulating protein levels could help shed light on the underlying mechanisms, prompting this exploratory study of 64 patients operated for colorectal cancer with anastomosis. Methods: Thirty-two cases who suffered a postoperative peritoneal infection were matched with 32 controls who had a complication-free postoperative stay. Proteins in serum samples at their first postoperative visit and at one year after surgery were analysed using proximity extension assays and enzyme-linked immunosorbent assays. Multivariate projection methods, adjusted for multiple testing, were used to compare levels between groups, and enrichment and network analyses were performed. Results: Seventy-seven proteins, out of 270 tested, were differentially expressed at a median sampling time of 41 days postoperatively. These proteins were all normalised one year after surgery. Many of the differentially expressed top hub proteins have known involvement in cancer progression, survival, invasiveness and metastasis. Over-represented pathways were related to cardiomyopathy, cell-adhesion, extracellular matrix, phosphatidylinositol-3-kinase/Akt (PI3K-Akt) and transforming growth factor beta (TGF-β) signaling. Conclusion: These affected proteins and pathways could provide clues as to why patients with peritoneal infection might suffer increased cancer recurrence, mortality and cardiovascular morbidity.

Pancreatic ductal adenocarcinoma (PDAC) is a major cause of cancer death that typically presents at an advanced stage. No reliable markers for early detection presently exist. The prominent tumor stroma represents a source of circulating biomarkers for use together with cancer cell-derived biomarkers for earlier PDAC diagnosis. CA19-9 and CEA (cancer cell-derived biomarkers), together with endostatin and collagen IV (stroma-derived) were examined alone, or together, by multivariable modelling, using pre-diagnostic plasma samples (n = 259 samples) from the Northern Sweden Health and Disease Study biobank. Serial samples were available for a subgroup of future patients. Marker efficacy for future PDAC case prediction (n = 154 future cases) was examined by both cross-sectional (ROC analysis) and longitudinal analyses. CA19-9 performed well at, and within, six months to diagnosis and multivariable modelling was not superior to CA19-9 alone in cross-sectional analysis. Within six months to diagnosis, CA19-9 (AUC = 0.92) outperformed the multivariable model (AUC = 0.81) at a cross-sectional level. At diagnosis, CA19-9 (AUC = 0.995) and the model (AUC = 0.977) performed similarly. Longitudinal analysis revealed increases in CA19-9 up to two years to diagnosis which indicates a window of opportunity for early detection of PDAC.