Spectral fluorescence lifetime imaging (S-FLIM) simultaneously deconvolves signal from multiple fluorophore species by leveraging both spectral and lifetime information. However, existing analyses still face multiple hurdles in decoding information collected from typical S-FLIM experiments. These include: using information from pre-calibrated spectra in environments that may differ from the cellular context in which S-FLIM experiments are performed; limitations arising from overlapping spectra; high photon budget requirements, typically about a hundred photons per pixel per species. Yet information on the spectra themselves are already encoded in the data and do not require pre-calibration. Moreover, efficient photon-by-photon analyses are possible reducing both the required photon budget and making it possible to use larger budgets in order to discriminate small differences in spectra to resolve spatially co-localized fluorophore species. To achieve this, we propose a framework, Bayes-S-FLIM, capable of simultaneously learning spectra and lifetimes photon-by-photon, using limited photon counts as low as 5000 photon to distinguish 3 species achieving high data efficiency. We demonstrate the proposed framework using synthetic and experimental data and show we can operate in limiting photon regimes and distinguish lifetimes with sub-nanosecond differences. Our synthetic data analysis suggests that we can deconvolve up to 9 species with heavily overlapped spectra.

Omics technologies such as genomics, transcriptomics, proteomics and metabolomics methods, have been instrumental in improving our understanding of complex biological systems by providing high-dimensional phenotypes of cell populations and single cells. Despite fast-paced advancements, these methods are limited in their ability to include a temporal dimension. Here, we introduce ESPRESSO (Environmental Sensor Phenotyping RElayed by Subcellular Structures and Organelles), a technique that provides single-cell, high-dimensional phenotyping resolved in space and time. ESPRESSO combines fluorescent labeling, advanced microscopy and image and data analysis methods to extract morphological and functional information from organelles at the single-cell level. We validate ESPRESSO’s methodology and its application across numerous cellular systems for the analysis of cell type, stress response, differentiation and immune cell polarization. We show that ESPRESSO can correlate phenotype changes with gene expression, and demonstrate its applicability to 3D cultures, offering a path to improved spatially and temporally resolved biological exploration of cellular states. ESPRESSO leverages functional information obtained from organelles for deep spatiotemporal phenotyping of single cells.

Spectral fluorescence lifetime imaging (S-FLIM) allows for the simultaneous deconvolution of signal from multiple fluorophore species by leveraging both spectral and lifetime information. However, existing analyses still face multiple difficulties in decoding information collected from typical S-FLIM experiments. These include: using information from pre-calibrated spectra in environments that may differ from the cellular context in which S-FLIM experiments are performed; limitations in the ability to deconvolute species due to overlapping spectra; high photon budget requirements, typically about a hundred photons per pixel per species. Yet information on the spectra themselves are already encoded in the data and do not require pre-calibration. What is more, efficient photon-by-photon analyses are possible reducing both the required photon budget and making it possible to use larger budgets in order to discriminate small differences in spectra to resolve spatially co-localized fluorophore species. To achieve this, we propose a Bayesian S-FLIM framework capable of simultaneously learning spectra and lifetimes photon-by-photon ultimately using limited photon counts and being highly data efficient. We demonstrate the proposed framework using a range of synthetic and experimental data and show that it can deconvolve up to 9 species with heavily overlapped spectra.

Pair correlation microscopy is a unique approach to fluorescence correlation spectroscopy that can track the long-range diffusive route of a population of fluorescent molecules in live cells with respect to intracellular architecture. This method is based on the use of a pair correlation function (pCF) that, through spatiotemporal comparison of fluctuations in fluorescence intensity recorded throughout a microscope data acquisition, enables changes in a molecule’s arrival time to be spatially mapped and statistically quantified. In this protocol, we present guidelines for the measurement and analysis of line scan pair correlation microscopy data acquired on a confocal laser scanning microscope (CLSM), which will enable users to extract a fluorescent molecule’s transport pattern throughout a living cell, and then quantify the molecular accessibility of intracellular barriers encountered or the mode of diffusion governing a molecular trafficking event. Finally, we demonstrate how this protocol can be extended to a two-channel line scan acquisition that, when coupled with a cross pCF calculation, enables a fluorescent molecule’s transport pattern to be selectively tracked as a function of complex formation with a spectrally distinct fluorescent ligand. For a skilled user of a CLSM, the line scan data acquisition and analysis described in this protocol will take 1–2 d, depending on the sample and the number of experiments to be processed. This protocol presents a method based on fluorescence correlation spectroscopy for tracking the diffusive route of fluorescent molecules in a live cell.

The secreted phospholipase A2 human group IIA (hGIIA) is overexpressed in prostate cancer (PCa), where its expression is closely aligned with malignancy. While its enzymatic activity is important in mediating innate immunity, here we highlight that hGIIA contributes to PCa pathology primarily through specific protein-protein interactions. We have developed cyclic peptides cF and c2, derived from the structure of hGIIA, that selectively inhibit these interactions and inhibit PCa growth. hGIIA interacts directly with epidermal growth factor receptor (EGFR), resulting in increased cytosolic PLA2-α activation and prostaglandin E2 production, which is suppressed by c2. Further, vimentin was identified to bind hGIIA in PCa cells, modulating hGIIA intracellular trafficking. c2 binds vimentin, blocking this interaction and initiating vimentin-mediated aggresome formation and apoptosis even in the absence of hGIIA. cF and c2 suppress androgen-sensitive, castrate-resistant and androgen-independent models of tumour growth in vivo at doses as low as 0.1 mg/kg, are non-toxic, orally bioavailable and cell-permeable. Critically, as with hGIIA, EGFR and vimentin are also increasingly expressed as PCa develops, cF and c2 may represent a novel therapeutic option for incurable metastatic castrate resistant PCa. Our findings identify hGIIA as an innate immune effector that regulates both inflammation and PCa progression and describe a novel class of hGIIA protein-protein interaction inhibitor with therapeutic potential in PCa.

Confocal microscopy is an important bio-imaging technique that increases the resolution using a spatial pinhole to block out-of-focus light. In theory, the maximum resolution and optical sectioning are obtained when the detection pinhole is fully closed, but this is prevented by the dramatic decrease in the signal reaching the detector. In image scanning microscopy (ISM) this limitation is overcome by the use of an array of point detectors rather than a single root detector. This, combined with pixel reassignment, increases the resolution of 2 over widefield imaging, with relatively little modification to the existing hardware of a laser-scanning microscope. Separation of photons by lifetime tuning (SPLIT) is a super-resolution technique, based on the phasor analysis of the fluorescent signal into an additional channel of the microscope. Here, we use SPLIT to analyze the information encoded within the array detectors distance for improving the resolution of ISM (SPLIT-ISM). We find that the lateral resolution can be increased of an additional 1.3 x with respect to the pixel-reassigned image with a concomitant increase in optical sectioning. We applied the SPLIT-ISM technique on biological images acquired by two currently available ISM systems: the Genoa Instruments PRISM and the Zeiss Airyscan. We evaluate the improvement provided by SPLIT-ISM through the QuICS algorithm, a quantitative tool based on image correlation spectroscopy. QuICS allows extracting three parameters related to the resolution, and contrast SNR of the image. We find that SPLIT-ISM provides an increase in spatial resolution for both the Genoa Instrument PRISM and the Zeiss Airyscan microscopes.

Understanding bacterial physiology in real-world environments requires noninvasive approaches and is a challenging yet necessary endeavor to effectively treat infectious disease. Bacteria evolve strategies to tolerate chemical gradients associated with infections. The DIVER (Deep Imaging Via Enhanced Recovery) microscope can image autofluorescence and fluorescence lifetime throughout samples with high optical scattering, enabling the study of naturally formed chemical gradients throughout intact biofilms. Using the DIVER, a long fluorescent lifetime signal associated with reduced pyocyanin, a molecule for electron cycling in low oxygen, was detected in low-oxygen conditions at the surface of Pseudomonas aeruginosa biofilms and in the presence of fermentation metabolites from Rothia mucilaginosa, which cocolonizes infected airways with P. aeruginosa. These findings underscore the utility of the DIVER microscope and fluorescent lifetime for noninvasive studies of bacterial physiology within complex environments, which could inform on more effective strategies for managing chronic infection.

Confocal microscopy is a bio-imaging technique which increases the resolution using a spatial pinhole to block out-of-focus light. In theory, the maximum resolution and optical sectioning is obtained when the detection pinhole is fully closed but this is prevented by the dramatic decrease of the signal reaching the detector. In image scanning microscopy (ISM) this limitation is overcome by using an array of point-detectors rather than a single detector. This, combined with pixel reassignment, increases the resolution of √2 over widefield imaging, with relatively little modification to the existing hardware of a laser-scanning microscope.

Fluorescence microscopy can provide valuable information about cell interior dynamics. Particularly, mean squared displacement (MSD) analysis is widely used to characterize proteins and sub-cellular structures’ mobility providing the laws of molecular diffusion. The MSD curve is traditionally extracted from individual trajectories recorded by single-particle tracking-based techniques. More recently, image correlation methods like iMSD have been shown capable of providing averaged dynamic information directly from images, without the need for isolation and localization of individual particles. iMSD is a powerful technique that has been successfully applied to many different biological problems, over a wide spatial and temporal scales. The aim of this work is to review and compare these two well-established methodologies and their performance in different situations, to give an insight on how to make the most out of their unique characteristics. We show the analysis of the same datasets by the two methods. Regardless of the experimental differences in the input data for MSD or iMSD analysis, our results show that the two approaches can address equivalent questions for free diffusing systems. We focused on studying a range of diffusion coefficients between D = 0.001 μ m 2 s −1 and D = 0.1 μ m 2 s −1 , where we verified that the equivalence is maintained even for the case of isolated particles. This opens new opportunities for studying intracellular dynamics using equipment commonly available in any biophysical laboratory.

In this work, we examine the use of environment-sensitive fluorescent dyes in fluorescence lifetime imaging microscopy (FLIM) biosensors. We screened merocyanine dyes to find an optimal combination of environment-induced lifetime changes, photostability, and brightness at wavelengths suitable for live-cell imaging. FLIM was used to monitor a biosensor reporting conformational changes of endogenous Cdc42 in living cells. The ability to quantify activity using phasor analysis of a single fluorophore (e.g., rather than ratio imaging) eliminated potential artifacts. We leveraged these properties to determine specific concentrations of activated Cdc42 across the cell.

Chromosome-containing micronuclei are a hallmark of aggressive cancers. Micronuclei frequently undergo irreversible collapse, exposing their enclosed chromatin to the cytosol. Micronuclear rupture catalyzes chromosomal rearrangements, epigenetic abnormalities, and inflammation, yet mechanisms safeguarding micronuclear integrity are poorly understood. In this study, we found that mitochondria-derived reactive oxygen species (ROS) disrupt micronuclei by promoting a noncanonical function of charged multivesicular body protein 7 (CHMP7), a scaffolding protein for the membrane repair complex known as endosomal sorting complex required for transport III (ESCRT-III). ROS retained CHMP7 in micronuclei while disrupting its interaction with other ESCRT-III components. ROS-induced cysteine oxidation stimulated CHMP7 oligomerization and binding to the nuclear membrane protein LEMD2, disrupting micronuclear envelopes. Furthermore, this ROS-CHMP7 pathological axis engendered chromosome shattering known to result from micronuclear rupture. It also mediated micronuclear disintegrity under hypoxic conditions, linking tumor hypoxia with downstream processes driving cancer progression.

When combined with spectral imaging, fluorescence lifetime imaging (FLIM) provides both spectral as well as lifetime properties of fluorophores critical in unmixing signals from fluorophores in complex subcellular environments. This multiplexed method, termed spectral-FLIM, has allowed us to discriminate between lifetimes too similar (sub-nanosecond differences) to be independently assessed by FLIM alone. Despite its clear advantages, spectral-FLIM remains limited in unmixing large numbers of fluorophore species hindering highly multiplexed imaging.

Microglia, the immune cells of the central nervous system, are dynamic and heterogenous cells. While single cell RNA sequencing has become the conventional methodology for evaluating microglial state, transcriptomics do not provide insight into functional changes, identifying a critical gap in the field. Here, we propose a novel organelle phenotyping approach in which we treat live human induced pluripotent stem cell-derived microglia (iMGL) with organelle dyes staining mitochondria, lipids, lysosomes and acquire data by live-cell spectral microscopy. Dimensionality reduction techniques and unbiased cluster identification allow for recognition of microglial subpopulations with single-cell resolution based on organelle function. We validated this methodology using lipopolysaccharide and IL-10 treatment to polarize iMGL to an "inflammatory" and "anti-inflammatory" state, respectively, and then applied it to identify a novel regulator of iMGL function, complement protein C1q. While C1q is traditionally known as the initiator of the complement cascade, here we use organelle phenotyping to identify a role for C1q in regulating iMGL polarization via fatty acid storage and mitochondria membrane potential. Follow up evaluation of microglia using traditional read outs of activation state confirm that C1q drives an increase in microglia pro-inflammatory gene production and migration, while suppressing microglial proliferation. These data together validate the use of a novel organelle phenotyping approach and enable better mechanistic investigation of molecular regulators of microglial state.

This study addresses challenges in Optical Metabolic Imaging due to dim signals, overlapping spectra, and similar lifetimes of NADH and FAD autofluorescent molecules. A Phasor-based S-FLIM-SHG microscope is introduced, enabling simultaneous acquisition of Hyperspectral Imaging (HSI), Fluorescence Lifetime Imaging Microscopy (FLIM), and Second Harmonic Generation imaging (SHG). The microscope's design efficiently detects scattered photons from complex samples and it is particularly competent at detection SHG signal. A novel 5D-snapshot (x, y, z, τ, λ). metabolic imaging method is proposed, significantly reducing acquisition times and enhancing measurement accuracy. The method's versatility is demonstrated across diverse sample types, with potential implications for advancing optical metabolic imaging capabilities.

Chromosomally unstable cancer cells are characterized by micronuclei, aberrant organelles containing missegregated chromosomes. Micronuclei differ from the primary nucleus in both nuclear envelope composition and biological processes. In fact, micronuclei often undergo irreversible rupture, exposing their DNA to the cytosol where it activates pro-metastatic pathways and catalyzing extensive heritable genomic and epigenetic rearrangements. Micronuclear collapse is thus a central event for tumor evolution and metastatic progression, and its consequences have been linked to poor-prognosis and therapy resistance; nonetheless, the mechanisms driving micronuclear rupture are still obscure.

Abstract Chromosomal instability and changes in the epigenome are defining features in most metastatic cancers. Our research links chromosomal missegregation, their containment in micronuclei, and subsequent rupture of the micronuclear envelope to histone post-translational modification aberrations. This relationship was across all tested human and murine cancer cell lines, as well as non-transformed cells. Furthermore, we discovered that the observed changes in histone PTMs were caused by either micronuclear rupture or the persistence of histone PTM status during cell division. Moreover, we revealed substantial chromatin accessibility differences in micronuclei. Notably, there is a distinct bias in chromatin accessibility between promoter regions compared to distal and intergenic regions of the genome, which aligned well with observed changes in histone modifications. The induction of chromosomal instability leads to widespread, heterogeneous, and heritable abnormalities in the epigenetic landscape of cancer cells. Therefore, on top of genomic copy number changes, chromosomal instability causes epigenetic reprogramming, which adds another layer to cancer heterogeneity. Citation Format: Albert S. Agustinus, Duaa Al-Rawi, Bhargavi Dameracharla, Ramya Raviram, Bailey S. Jones, Stephanie Stransky, Lorenzo Scipioni, Jens Luebeck, Melody Di Bona, Danguole Norkunaite, Robert M. Myers, Mercedes Duran, Seongmin Choi, Britta Weigelt, Shira Yomtoubian, Andrew McPherson, Eleonore Toufektchan, Kristina Keuper, Paul S. Mischel, Vivek Mittal, Sohrab P. Shah, John Maciejowski, Zuzana Storchova, Enrico Gratton, Peter Ly, Dan Landau, Mathieu F. Bakhoum, Richard P. Koche, Simone Sidoli, Vineet Bafna, Yael David, Samuel F. Bakhoum. Chromosomal instability causes epigenetic aberrations in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4293.

Metabolic plasticity, i.e., the capability of cells to modify their metabolic state, is a hallmark of cancer and an important factor in the formation of metastases as well as a major contributor to chemoresistance and tumor recurrence. Furthermore, the tumor microenvironment, namely the collection of environmental properties (pH, nutrient availability, fatty acids) and cell types that surround the main tumor mass, is regarded to as the main driver of the metabolic state of tumor cells. Unfortunately, no technique can non-invasively assess the metabolic state of single cells in living samples, limiting our understanding of the heterogeneity and dynamics of metabolic state transitions. Here, we apply a technique, named ESPRESSO (Environmental Sensors Profiling Relayed by Subcellular Structures and Organelles) that combines organelle-specific environment-sensitive probes (ESPs) with hyperspectral imaging and quantitative bioimage analysis. We use a mixture of lipid droplet-, mitochondria- and lysosome-specific ESPs to quantify of morphological (e.g., number, size, organization) and functional (e.g., membrane potential, pH, polarity) characteristics to identify the metabolic state in single, living cells. To understand the differences in metabolic plasticity of non-tumorigenic breast epithelial (MCF10a) and triple negative breast cancer (MDA-MB-231) cells, we determined their metabolic state with ESPRESSO under a variety of stress and environmental conditions, identifying the characteristic metabolic signature of the cell lines under those conditions. We further investigated the response to diverse chemotherapy drugs (doxorubicin, paclitaxel, curcumin) in an effort to understand their effect on cancer and healthy cells, highlighting the environmental conditions that would favor the resistance to a specific drug treatment. Taken together, we provide a framework to identify the metabolic response of cancer and non-tumorigenic cells under different drugs, stressors and environmental conditions, providing in-depth characterization of their metabolic response at the single cell level.

Fluorescence lifetime imaging captures the spatial distribution 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, which 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 or whose duration may be long enough to be comparable to the inter-pulse 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 nonpara-metrics (BNP). We benchmark our algorithm, termed BNP lifetime analysis, using a range of synthetic and experimental data. Moreover, we show that the BNP lifetime analysis method can distinguish and deduce lifetimes using photon counts as small as 500.