Biology subjects

Vallmitjana, A.

Publications and source records attributed to Vallmitjana, A..

6 recordsLinked to original sources

bioRxiv2025

GSLab: Open-Source Platform for Advanced Phasor Analysis in Fluorescence Microscopy

GSLab addresses the need for effective image analysis tools in fluorescence microscopy by providing an open-source platform that enhances traditional phasor analysis with advanced features. Key capabilities include machine learning-based clustering, real-time monitoring, and quantitative unmixing of fluorescent species. Designed for both commercial and custom systems, GSLab provides researchers with comprehensive lifetime and spectral phasor image analysis tools to tackle complex biological problems.

bioinformatics↗

bioRxiv2024

A versatile reporter system to study cell-to-cell and cell-free bovine leukemia virus infection

Bovine leukemia virus (BLV) is a B-lymphotropic oncogenic retrovirus of the genus Deltaretrovirus that infects dairy cattle worldwide and is the causative agent of enzootic bovine leukosis. BLV demonstrates remarkably low efficiency in infecting cells via free viral particles derived from infected B cells, as virions are rarely detected in the bloodstream of infected cattle. However, transmission efficacy significantly increases upon the establishment of direct cell-to-cell interactions. Syncytium formation assays are the main tool in the study of BLV infectivity. Although this traditional method is highly robust, the complexity of visually counting syncytia poses a significant technical challenge. Using lentiviral vectors, we generated a stable reporter cell line, in which the GFP reporter gene is under the control of the full-length BLV-LTR. We have demonstrated that the BLV-Tax protein and histone deacetylases inhibitors, such as VPA and TSA, can transactivate the BLV LTR. Upon co-culturing the reporter cell line with BLV-infected cells, fluorescent syncytia can be visualized. By implementing automated scanning and image acquisition using a confocal microscope, together with the development of an analysis software, we can detect and measure single GFP cells and fluorescent multinucleated cells. In summary, our reporter cell line, combined with the development of analysis software, is a useful tool for understanding the role of cell fusion and cell-free mechanism of transmission in BLV infection.

microbiology↗

bioRxiv2022

Building Fluorescence Lifetime Maps Photon-by-photon by Leveraging Spatial Correlations

Fluorescence lifetime imaging microscopy (FLIM) has become a standard tool in the quantitative analysis of sub-cellular environments. However, quantitative FLIM analyses face several challenges. First, spatial correlations between pixels are often ignored as signal from individual pixels is analyzed independently thereby limiting spatial resolution. Second, existing methods deduce photon ratios instead of absolute lifetime maps. Next, the number of lifetime components contributing to the signal is unknown, while excited state lifetimes with <1 ns difference are difficult to discriminate. Finally, existing analyses require high photon budgets, and often cannot rigorously propagate experimental uncertainty into values over lifetime maps and number of components involved. To overcome all of these challenges simultaneously and self-consistently at once, we propose the first doubly nonparametric framework. That is, we learn the number of fluorescent species (through beta-Bernoulli process priors) and absolute lifetime maps of these species (through Gaussian process priors) by leveraging information from pulses not leading to observed photon. We benchmark our algorithm using a broad range of synthetic and experimental data and demonstrate its robustness across a number of scenarios including cases where we recover lifetime differences between components as small as 0.3 ns with merely 1000 photons.

biophysics↗

bioRxiv2022

Fluorescence Lifetime: Beating the IRF and interpulse window

Fluorescence lifetime imaging (FLIM) has been essential in capturing spatial distributions of chemical species across cellular environments employing pulsed illumination confocal setups. However, quantitative interpretation of lifetime data continues to face critical challenges. For instance, fluorescent species with known in vitro excited state lifetimes may split into multiple species with unique lifetimes when introduced into complex living environments. What is more, mixtures of species, that may be both endogenous and introduced into the sample, may exhibit; 1) very similar lifetimes; as well as 2) wide ranges of lifetimes including lifetimes shorter than the instrumental response function (IRF) or whose duration may be long enough to be comparable to the interpulse window. By contrast, existing methods of analysis are optimized for well-separated and intermediate lifetimes. Here we broaden the applicability of fluorescence lifetime analysis by simultaneously treating unknown mixtures of arbitrary lifetimes- outside the intermediate, goldilocks, zone-for data drawn from a single confocal spot leveraging the tools of Bayesian nonparametrics (BNP). We benchmark our algorithm, termed BNP-lifetime analysis of BNP-LA, using a range of synthetic and experimental data. Moreover, we show that the BNP-LA method can distinguish and deduce lifetimes using photon counts as small as 500.

biophysics↗

bioRxiv2022

Phasor-Based Multi-Harmonic Unmixing for in-vivo Hyperspectral Imaging

Hyperspectral imaging (HSI) is a paramount technique in biomedical science, however, unmixing and quantification of each spectral component is a challenging task. Traditional unmixing relies on algorithms that need spectroscopic parameters from the fluorescent species in the sample. The phasor-based multi-harmonic unmixing method requires only the empirical measurement of the pure species to compute the pixel-wise photon fraction of every spectral component. Using simulations, we demonstrate the feasibility of the approach for up to 5 components and explore the use of adding a 6th unknown component representing autofluorescence. The simulations show that the method can be successfully used in typical confocal imaging experiments (with pixel photon counts between 101 and 103). As a proof of concept, we tested the method in living cells, using 5 common commercial dyes for organelle labeling and we easily and accurately separate them. Finally, we challenged the method by introducing a solvatochromic probe, 6-Dodecanoyl-N,N-dimethyl-2-naphthylamine (LAURDAN), intended to measure membrane dynamics on specific subcellular membrane-bound organelles by taking advantage of the linear combination between the organelle probes and LAURDAN. We succeeded in monitoring the membrane order in the Golgi apparatus, Mitochondria, and plasma membrane in the same in-vivo cell and quantitatively comparing them. The phasor-based multi-harmonic unmixing method can help expand the outreach of HSI and democratize its use by the community for it does not require specialized knowledge.

biophysics↗

bioRxiv2021

Spatial transcriptomics using combinatorial fluorescence spectral and lifetime encoding, imaging and analysis

Multiplexed mRNA profiling in the spatial context provides important new information enabling basic research and clinical applications. Unfortunately, most existing spatial transcriptomics methods are limited due to either low multiplexing or assay complexity. Here, we introduce a new spatialomics technology, termed Multi Omic Single-scan Assay with Integrated Combinatorial Analysis (MOSAICA), that integrates in situ labeling of mRNA and protein markers in cells or tissues with combinatorial fluorescence spectral and lifetime encoded probes, spectral and time-resolved fluorescence imaging, and machine learning-based target decoding. This technology is the first application combining the biophotonic techniques, Spectral and Fluorescence Lifetime Imaging and Microscopy (FLIM), to the field of spatial transcriptomics. By integrating the time dimension with conventional spectrum-based measurements, MOSAICA enables direct and highly-multiplexed in situ spatial biomarker profiling in a single round of staining and imaging while providing error correction removal of background autofluorescence. We demonstrate mRNA encoding using combinatorial spectral and lifetime labeling and target decoding and quantification using a phasor-based image segmentation and machine learning clustering technique. We then showcase MOSAICAs multiplexing scalability in detecting 10-plex targets in fixed colorectal cancer cells using combinatorial labeling of only five fluorophores with facile error-correction and removal of autofluorescent moieties. MOSAICAs analysis is strongly correlated with sequencing data (Pearsons r = 0.9) and was further benchmarked using RNAscopeand LGC Stellaris. We further apply MOSAICA for multiplexed analysis of clinical melanoma Formalin-Fixed Paraffin-Embedded (FFPE) tissues that have a high degree of tissue scattering and intrinsic autofluorescence, demonstrating the robustness of the approach. We then demonstrate simultaneous co-detection of protein and mRNA in colorectal cancer cells. MOSAICA represents a simple, versatile, and scalable tool for targeted spatial transcriptomics analysis that can find broad utility in constructing human cell atlases, elucidating biological and disease processes in the spatial context, and serving as companion diagnostics for stratified patient care.

bioengineering↗