Search Results (68)

Background/Objectives: TP53 mutation or deletion status is important for determining cellular responses to DNA-damaging drugs. Oxaliplatin (OXA) is combined with the fluoropyrimidine (FP) drug 5-fluorouracil (5-FU) in the FOLFOX regimen used to treat advanced colorectal cancer (CRC). However, the effects of TP53 deletion on 5-FU + OXA synergy are not well known. We investigated potential synergy between OXA and 5-FU and compared it with OXA synergy with a novel polymeric FP, CF10, in four cell lines harboring either wild-type (WT) or TP53-null status. Methods: Using CompuSyn and the highest single agent (HSA) models, we compared synergy between CF10 and OXA (COXA) and between 5-FU and OXA (FOXA). Cell cycle analysis was performed, as was Western blot quantification of canonical DNA damage pathway proteins. Likewise, immunofluorescent and confocal analysis allowed us to compare topoisomerase 1 cleavage complex and double-strand DNA break formation. Results: COXA synergy displayed minimal TP53 dependence with greatly improved potency compared to FOXA. COXA synergy resulted from OXA increasing: (i) Topoisomerase 1 (Top1) cleavage complex formation; (ii) DNA double-strand breaks (DSBs), and (iii) Checkpoint Kinase 1 and 2 (p-Chk1/2) phosphorylation, consistent with increased replication stress. Additionally, increased S-phase entry in TP53-null cells enhanced synergy between CF10, 5-FU, and OXA as S-phase drugs. Conclusions: Our results demonstrate that OXA synergizes with CF10 more effectively than with 5-FU through enhanced replication stress in both WT and TP53-null cells by causing greater Top1-mediated DNA double-strand breaks. Our studies provide a foundation for further testing of this combination in an orthotopic liver metastatic setting and eventual clinical development. Full article

Background: Host cell proteins (HCPs) are process-related impurities that must be monitored in biopharmaceutical products due to their potential impact on product quality and patient safety. Targeted LC–MS/MS approaches such as multiple reaction monitoring (MRM) enable protein-specific HCP quantification but are difficult to apply in highly multiplexed assays because of retention time (RT) variability across complex bioprocess matrices. Methods: Here, we show that conventional RT-scheduled MRM workflows lack transferability when applied to heterogeneous drug substances and process intermediates. Using a targeted assay comprising 240 peptides corresponding to 97 CHO-derived HCPs, RT shifts of several minutes resulted in truncated chromatographic peaks and peptide signal loss, even when wide scheduling windows were used. To overcome this limitation, a scout-triggered MRM (st-MRM) acquisition strategy based on event-driven monitoring was implemented. Results: This approach enabled robust peptide detection across diverse matrices within a single injection, without method re-optimization. Absolute quantification using stable isotope-labeled peptides spanned six orders of magnitude, with HCPs quantified down to 2.9 ppm in purified drug substances. Conclusion: Overall, st-MRM improves the robustness and transferability of highly multiplexed targeted proteomics workflows for HCP analysis. Full article

A detailed mathematical model has been developed for the dynamics of hepatitis B virus (HBV) infection in a single cell. It provides a platform for a better quantitative understanding of the biochemical kinetics of the HBV lifecycle. The model is used to study the sensitivity of virus growth, providing a clear ranking of intracellular virus replication processes with respect to their contribution to net viral production. The stochastic formulation of the model enables the quantification of the variability characteristics in viral production, the probability of productive infection and the secretion of protein- and genome-deficient viral particles. An essential difference in infection efficiency between deterministic and stochastic models has been revealed. For example, in the case of $\mathrm{MOI}=1$, the mean value of the total number of mature virions released during the lifecycle of the infection in the stochastic model is 1.06, whereas, in the deterministic model, its value is less than one thousandth and thus close to 0. The model is also used to quantitatively predict the effect of combinations of direct-acting antivirals, such as small interfering RNAs, capsid inhibitors and nucleoside analogues. The model shows that the inhibitory effect of siRNA on viral production is approximately two orders of magnitude higher than that of nucleoside analogues and capsid inhibitors. Full article

DNMT1 variants are linked to complex neurodegenerative syndromes, yet their variant-specific functional and epigenomic consequences remain poorly defined. DNMT1 variants were identified in eight patients using gene-panel or whole-exome sequencing. Functional effects were assessed by site-directed mutagenesis and transient expression in HEK293T cells. Genome-wide methylation profiling of peripheral blood leukocyte DNA was performed using Nanopore sequencing, enabling direct quantification of 5-methylcytosine (5mC). CpG island-level differential methylation and gene set enrichment analysis (GSEA) were conducted. Variants in the replication foci targeting sequence (RFTS) domain (p.Y511H, p.Y540C, p.H569R) exhibited reduced DNMT1 protein expression, decreased enzymatic activity, and cytosolic aggregation. Variants in the C-terminal catalytic domain (p.A1334V and p.P1546S) showed reduced protein expression with relatively mild enzymatic impairment. Patients carrying the p.Y511H variant demonstrated a significant reduction in global 5mC levels compared with controls. Principal component analysis revealed distinct methylomic profiles separating most patients from controls, with marked intra- and inter-familial heterogeneity. CpG island-level analysis identified a single significantly hypomethylated region in p.Y511H carriers, and GSEA revealed differential enrichment of multiple Gene Ontology biological pathways. This study defines domain-dependent functional effects of DNMT1 variants and provides the first nanopore-based methylome analysis, revealing variant-specific and heterogeneous epigenomic alterations. Full article

Background: Dictyostelium discoideum is widely used in developmental and evolutionary biology due to its ability to transition from a single cell to a multicellular organism in response to starvation. While transcriptome information across its life cycle is widely available, only early-stage data exist at the proteome level. This study characterizes and compares the proteomes of D. discoideum cells at the vegetative, aggregation, mound, culmination and fruiting body stages. Methods: Samples were collected from cells developing synchronously on nitrocellulose filters. Proteins were extracted and digested with trypsin, and peptides were analyzed by liquid chromatography–tandem mass spectrometry. Data were processed using Proteome Discoverer™ for protein identification and label-free quantification. Results: A total of 4502 proteins were identified, of which 1848 (41%) were present across all stages. Pairwise comparisons between adjacent stages revealed clear transitions, the largest ones occurring between the culmination and fruiting body and between the fruiting body and vegetative stage, involving 29% and 52% of proteins, respectively. Hierarchical clustering assigned proteins to one of nine clusters, each displaying a distinct pattern of abundances across the life cycle. Conclusions: This study presents the first complete developmental proteomic time series for D. discoideum, revealing changes that contribute to multicellularity, cellular differentiation and morphogenesis. Full article

A large number of regulatory proteins are found in both the cytoplasm and the nucleus. Changes in their nuclear abundance are important for cellular signalling, biological activity, and disease mechanisms. Accurate quantification of nuclear staining is therefore essential in studies of cellular function, therapeutic targeting, drug design, and drug resistance. However, manual scoring is time-consuming, unsuitable for high-throughput applications, and introduces potential bias. As expected, manual scoring by six observers with varying levels of expertise led to highly variable results. Moreover, it was far from achieving good interobserver reliability. To overcome these limitations, LoQANT (Localisation and Quantification of Antigen Nuclear sTaining), an open, freely available ImageJ plugin, was developed for reliable and efficient quantification of nuclear signals. LoQANT is a single cell-based approach to assess the proportion of cells with a positive nuclear signal, independent of cytoplasmic staining, in both immunohistochemically and fluorescently stained samples across various sample types. It also provides semiquantitative and quantitative measurements of nuclear staining intensity. The script, its version for batch analysis, and complete user guide are available at GitHub. Full article

Cocultivation of microalgae and aerobic methanotrophs represents an emerging biotechnology platform to produce high-protein biomass, yet quantifying individual species in mixed cultures remains challenging. Here, we present a rapid, low-cost method—differential sedimentation, optical density, and fluorescence (DSOF)—to determine the abundance of coculture members. DSOF exploits differences in cell size and pigment autofluorescence between the thermoacidophilic microalga and methanotrophic species Galdieria sp. RTK37.1 and Methylacidiphilum sp. RTK17.1, respectively, to selectively sediment algal cells and estimate population contributions via OD₆₀₀ and phycocyanin fluorescence. Evaluation with model suspensions across a wide cell density range (0 ≤ [Galdieria]: ≤ 3.23 A.U., and 0 ≤ [Methylacidiphilum] ≤ 1.54 A.U.) showed strong agreement with known values, with most absolute errors < 0.1 A.U. and relative errors < 10% at moderate biomass levels. Application to live batch cocultures under microalga or methanotroph growth-suppressed conditions, and during simultaneous growth, demonstrated accurate tracking of population dynamics and revealed enhanced methanotroph growth in the presence of oxygenic microalgae. While DSOF accuracy decreases at very concentrated biomass (>2.0 A.U. for Galdieria) or under nitrogen-limiting conditions, the model provides a practical, scalable alternative to more complex, invasive or expensive techniques, enabling near real-time monitoring of microalgae–methanotroph cocultures. Full article

Nanobodies, or single-domain antibodies (V_HHs) from camelid heavy-chain-only antibodies, offer significant advantages in therapeutic and diagnostic applications due to their small size and ability to bind cryptic protein epitopes inaccessible to conventional antibodies. In this study, we examined nanobodies specific to regions of the SARS-CoV-2 spike glycoprotein, including the receptor-binding domain (RBD), N-terminal domain (NTD), and subunit 2 (S2). Using flow virometry, a high-throughput technique for viral quantification, we achieved the efficient detection of pseudotyped viruses expressing the spike glycoprotein. RBD-targeting nanobodies showed the most effective staining, followed by NTD-targeting ones, while S2-specific nanobodies exhibited limited resolution. The simple genetic structure of nanobodies enables the creation of multimeric formats, improving binding specificity and avidity. Bivalent V_HH-Fc constructs (V_HHs fused to the Fc region of human IgG) outperformed monovalent formats in resolving viral particles from background noise. However, S2-specific monovalent V_HHs demonstrated improved staining efficiency, suggesting their smaller size better accesses restricted antigenic sites. Furthermore, direct staining of cell supernatants was possible without virus purification. This versatile nanobody platform, initially developed for antiviral therapy against SARS-CoV-2, can be readily adapted for flow virometry applications and other diagnostic assays. Full article

Characterizing biomolecular receptor–ligand interactions is critical for research and development. However, performing analyses in complex, biologically relevant matrices, such as serum, remains challenging due to non-specific binding that often impairs measurements. Here, we evaluated Focal Molography (FM) for determining K_D and kinetic constants in comparison to gold-standard methods using single-domain heavy-chain antibodies in various systems. FM provided kinetic constants highly comparable to SPR and BLI in standard buffers containing blocking proteins, with K_Ds of soluble CD4 (sCD4) interactions within a 2.4-fold range across technologies. In buffers lacking blocking proteins, FM demonstrated greater robustness against non-specific binding and rebinding effects. In serum, FM exhibited stable baseline signals, unlike SPR and BLI, and yielded K_Ds of sCD4 interaction in 50% Bovine Serum within a 1.8-fold range of those obtained in standard buffers. For challenging molecules prone to non-specific binding (Granzyme B), FM successfully determined kinetic constants without external referencing. Finally, FM enabled direct analyte quantification in complex matrices. sCD4 quantification in cell culture media and 50% FBS showed recovery rates of 97.8–100.3% with an inter-assay CV below 1.3%. This study demonstrates the high potential of FM for kinetic affinity determination and biomarker quantification in complex matrices, enabling reliable measurements under biologically relevant conditions. Full article

Oxidative phosphorylation and glycolysis are the main ATP-generating pathways in cell metabolism. The balance between these two pathways is frequently altered to carry out cell-specific activities in response to stimuli involving activation, proliferation, or differentiation. Despite being a useful tool for researching metabolic profiles in real time in relatively small numbers of cancer cells, the main Agilent Seahorse XF Pro Analyzer (Agilent Technologies, Santa Clara, CA, USA) guideline is currently not fully detailed in the distinction between suspensions vs. adherent cancer cells. This article provides step-by-step protocols for profiling metabolic fluxes in suspension vs. adherent cancer cells via Seahorse technology, including adjustments for normalisation of data on the basis of the number of viable cells or the total protein content. Owing to the adaptations of plates, reagents, cell count, and protein quantification, it is possible to (i) analyse both adherent and suspension cells with a single instrument; (ii) conduct all experiments in 96-well plates, thus using fewer cells, media, and reagents; (iii) determine the effect of a drug or compound directly on cell metabolism; (iv) normalise data on the basis of the number of viable cells or the total protein content via a spectrophotometer; and (v) achieve notable savings in cost and time. Full article

In this paper, we present a comparative analysis of the transcriptomic profile of three different human cell types: hematopoietic stem cells (HSCs), bone marrow-derived mesenchymal stem cells (MSCs) and fibroblasts (FIBs). The work aims to identify unique genes that are differentially expressed as specific markers of bone marrow-derived MSCs, and to achieve this undertakes a detailed analysis of three independent datasets that include quantification of the global gene expression profiles of three primary cell types: HSCs, MSCs and FIBs. A robust bioinformatics method, called GlobalTest, is used to assess the specific association between one or more genes expressed in a sample and the outcome variable, that is, the ‘cell type’ provided as a single univariate response. This outcome variable is predicted for each sample tested, based on the expression profile of the specific genes that are used as input to the test. The precision of the tests is calculated along with the statistical sensitivity and specificity for each gene in each dataset, yielding four genes that mark MSCs with high accuracy. Among these, the best performer is the protein-coding gene Transgelin (TAGLN, Gene ID: 6876) (with a Positive Predictive Value > 0.96 and FDR < 0.001), which identifies MSCs better than any of the currently used standard markers: ENG (CD105), THY1 (CD90) or NT5E (CD73). The results are validated by RT-qPCR, providing novel gene biomarkers specific for human MSCs. Full article

Single-cell analysis provides an overwhelming strategy for revealing cellular heterogeneity and new perspectives for understanding the biological function and disease mechanism. Moreover, it promotes the basic and clinical research in many fields at a single-cell resolution. A digital polymerase chain reaction (dPCR) is an absolute quantitative analysis technology with high sensitivity and precision for DNA/RNA or protein. With the development of microfluidic technology, digital PCR has been used to achieve absolute quantification of single-cell gene expression and single-cell proteins. For single-cell specific-gene or -protein detection, digital PCR has shown great advantages. So, this review will introduce the significance and process of single-cell analysis, including single-cell isolation, single-cell lysis, and single-cell detection methods, mainly focusing on the microfluidic single-cell digital PCR technology and its biological application at a single-cell level. The challenges and opportunities for the development of single-cell digital PCR are also discussed. Full article

The long-read RNA sequencing developed by Oxford Nanopore Technologies provides a direct quantification of transcript isoforms, thereby making it possible to present alternative splicing (AS) profiles as arrays of single splice variants with different abundances. Additionally, AS profiles can be presented as arrays of genes characterized by the degree of alternative splicing (the DAS—the number of detected splice variants per gene). Here, we successfully utilized the DAS to reveal biological pathways influenced by the alterations in AS in human liver tissue and the hepatocyte-derived malignant cell lines HepG2 and Huh7, thus employing the mathematical algorithm of gene set enrichment analysis. Furthermore, analysis of the AS profiles as abundances of single splice variants by using the graded tissue specificity index τ provided the selection of the groups of genes expressing particular splice variants specifically in liver tissue, HepG2 cells, and Huh7 cells. The majority of these splice variants were translated into proteins products and appeal to be in focus regarding further insights into the mechanisms underlying cell malignization. The used metrics are intrinsically suitable for transcriptome-wide AS profiling using long-read sequencing. Full article

Assaying changes in the amount of DNA in single cells is a well-established method for studying the effects of various perturbations on the cell cycle. A drawback of this method is the need for a fixation procedure that does not allow for in vivo study nor simultaneous monitoring of additional parameters such as fluorescence of tagged proteins or genetically encoded indicators. In this work, we report on a method of Histone Abundance Quantification (HAQ) of live yeast harboring a GFP-tagged histone, Htb2. We show that it provides data highly congruent with DNA levels, both in Saccharomyces cerevisiae and Ogataea polymorpha yeasts. The protocol for the DNA content assay was also optimized to be suitable for both Ogataea and Saccharomyces yeasts. Using the HAQ approach, we demonstrate the expected effects on the cell cycle progression for several compounds and conditions and show usability in conjunction with additional fluorophores. Thus, our data provide a simple approach that can be utilized in a wide range of studies where the effects of various stimuli on the cell cycle need to be monitored directly in living cells. Full article

Biomolecular condensates are crucial compartments within cells, relying on their material properties for function. They form and persist through weak, transient interactions, often undetectable by classical biochemical approaches. Hence, microscopy-based techniques have been the most reliable methods to detail the molecular mechanisms controlling their formation, material properties, and alterations, including dissolution or phase transitions due to cellular manipulation and disease, and to search for novel therapeutic strategies targeting biomolecular condensates. However, technical challenges in microscopy-based analysis persist. This paper discusses imaging, data acquisition, and analytical methodologies’ advantages, challenges, and limitations in determining biophysical parameters explaining biomolecular condensate formation, dissolution, and phase transitions. In addition, we mention how machine learning is increasingly important for efficient image analysis, teaching programs what a condensate should resemble, aiding in the correlation and interpretation of information from diverse data sources. Influenza A virus forms liquid viral inclusions in the infected cell cytosol that serve as model biomolecular condensates for this study. Our previous work showcased the possibility of hardening these liquid inclusions, potentially leading to novel antiviral strategies. This was established using a framework involving live cell imaging to measure dynamics, internal rearrangement capacity, coalescence, and relaxation time. Additionally, we integrated thermodynamic characteristics by analysing fixed images through Z-projections. The aforementioned paper laid the foundation for this subsequent technical paper, which explores how different modalities in data acquisition and processing impact the robustness of results to detect bona fide phase transitions by measuring thermodynamic traits in fixed cells. Using solely this approach would greatly simplify screening pipelines. For this, we tested how single focal plane images, Z-projections, or volumetric analyses of images stained with antibodies or live tagged proteins altered the quantification of thermodynamic measurements. Customizing methodologies for different biomolecular condensates through advanced bioimaging significantly contributes to biological research and potential therapeutic advancements. Full article