Cellular & Molecular Imaging

Purpose: Retinal ganglion cell (RGC) loss in glaucoma occurs in a large fraction of patients even after intraocular pressure (IOP) is reduced. Mitochondrial dysfunction is a key mechanism that links elevated IOP to RGC degeneration. We tested whether HDAP2, a novel high-density aromatic peptide that binds cardiolipin to stabilize mitochondrial membranes, can protect RGCs in the DBA/2J mouse model. Methods: DBA/2J mice received HDAP2 (3 mg/kg, intraperitoneally, every other day) starting at 4 months of age for 8 months. IOP was measured each month to track pressure exposure. RGC survival was assessed by counting RBPMS-stained cells in retinal wholemounts and optic nerve axons in semithin toludine blue-stained sections. Results: HDAP2-treated retinas had ~49% more RGCs than untreated retinas at similar pressure exposures (p = 0.0063; F(2,59) = 5.524). At mild IOP exposure, HDAP2 preserved 58% more RGCs compared with untreated retinas, and at high IOPs, RGC survival was 180% greater. Kaplan–Meier analysis indicated that HDAP2 increased the threshold for severe RGC loss by 29 mmHg and reduced the chance of developing severe RGC degeneration by a factor of 4.6. Optic nerve axons from treated retinas were also well preserved, with axon morphologies appearing indistinguishable from controls. Axon size distributions did not change significantly among the treatment groups, suggesting that protection by HDAP2 was similar among RGC subtypes. Conclusions: HDAP2 preserved both RGCs and axons in a pressure-dependent manner and increased tolerance to IOP. These results suggest that HDAP2 may complement pressure-lowering therapy, including for normal-tension and treatment-refractory glaucoma.

Iron (Fe) homeostasis is precisely regulated to prevent iron imbalance. The basic helix-loop-helix (bHLH) transcription factor bHLH104, a member of bHLH subgroup IVc, activates Fe uptake. bHLH104 is negatively controlled by the Fe-binding E3 ligase BRUTUS (BTS) that interacts with the C-terminus of bHLH104, according to predictions with involvement of the last three amino acids proline-alanine-alanine (PAA). Yet, experimental evidence for the PAA interaction site and importance for post-translational regulation are lacking. Here, we demonstrate the role of PAA for plant growth and bHLH104 regulation. Transgenic plants expressing a bHLH104 variant lacking the terminal PAA (b104^dPAA) have a constitutive Fe deficiency response. Strong accumulation of Fe causes b104^dPAA plants to have abnormal shoot and root development and delayed flowering, accompanied by mis-regulated Fe response and upregulated Fe acquisition genes. The severity of Fe accumulation phenotypes coincides with bHLH104 protein amounts. In plants, bHLH104 levels are controlled by proteasomal degradation in a PAA-dependent manner. Cell assays show that the PAA sequence is required for bHLH104 interaction with BTS and subsequent ubiquitination. Taken together, bHLH104 requires the terminal PAA to be post-translationally controlled by BTS. Manipulation of bHLHIVc protein PAA sequence represents a biofortification strategy for crop plants.

Fluorescence microscopy has been widely used to reveal the spatial distribution of specifically labeled molecules, but it is blind to cellular context. Quantitative phase contrast microscopy (QPC) provides such complementary information. Here we have developed a platform for correlative imaging that combines the QPC technique of orientation-independent differential interference contrast (OI-DIC) microscopy with single-molecule super-resolution fluorescence microscopy. We demonstrate a detection sensitivity of 0.05 nm optical path difference (0.17 nm s1/2 exposure-normalized sensitivity), sufficient to detect single microtubules, and show its capability of 3D fixed and live-cell super-resolution fluorescence imaging in the cellular context. Additionally, we report deep-learning enabled digital staining, identifying nuclei, mitochondria and lipid droplets from OI-DIC data and demonstrate the potential of this approach for long-term live-cell imaging of organelles of interest without the need for fluorescence. The presented design can be easily integrated into most fluorescence microscopes and is readily adoptable by microscopy labs.

Fluorescence microscopy has been widely used to reveal the spatial distribution of specifically labeled molecules, but it is blind to cellular context. Quantitative phase contrast microscopy (QPC) provides such complementary information. Here we have developed a platform for correlative imaging that combines the QPC technique of orientation-independent differential interference contrast (OI-DIC) microscopy with single-molecule super-resolution fluorescence microscopy. We demonstrate a detection sensitivity of 0.05 nm optical path difference (0.17 nm s1/2 exposure-normalized sensitivity), sufficient to detect single microtubules, and show its capability of 3D fixed and live-cell super-resolution fluorescence imaging in the cellular context. Additionally, we report deep-learning enabled digital staining, identifying nuclei, mitochondria and lipid droplets from OI-DIC data and demonstrate the potential of this approach for long-term live-cell imaging of organelles of interest without the need for fluorescence. The presented design can be easily integrated into most fluorescence microscopes and is readily adoptable by microscopy labs.

Fluorescence microscopy has been widely used to reveal the spatial distribution of specifically labeled molecules, but it is blind to cellular context. Quantitative phase contrast microscopy (QPC) provides such complementary information. Here we have developed a platform for correlative imaging that combines the QPC technique of orientation-independent differential interference contrast (OI-DIC) microscopy with single-molecule super-resolution fluorescence microscopy. We demonstrate a detection sensitivity of 0.05 nm optical path difference (0.17 nm s1/2 exposure-normalized sensitivity), sufficient to detect single microtubules, and show its capability of 3D fixed and live-cell super-resolution fluorescence imaging in the cellular context. Additionally, we report deep-learning enabled digital staining, identifying nuclei, mitochondria and lipid droplets from OI-DIC data and demonstrate the potential of this approach for long-term live-cell imaging of organelles of interest without the need for fluorescence. The presented design can be easily integrated into most fluorescence microscopes and is readily adoptable by microscopy labs.

Satellite DNA is long arrays of tandem repetitive DNA located often near the centromeres of chromosomes, whose function, or lack of, has been debated since its discovery. Although situated in heterochromatin, satellite DNA may be expressed as long noncoding RNAs (lncRNAs). Although there are a few examples of satellite lncRNAs being characterized, and functions suggested, how widespread and functionally important they may be for developmental processes is not understood. Here, we take an evolutionary approach to investigate satellite lncRNA expression in Drosophila spp. ovaries, a tissue whose development is well-characterized but where satellite expression has only been minimally explored. Using a publicly-available total RNAseq dataset, we find that 118/156 surveyed satellite DNAs were expressed across 10 species, with 33 satellites having high expression over 20 RPM. However, all but two of these expressed satellites (AAACTAC in D. virilis and ACAGACAGACAGG in D. ananassae) had higher read counts in a sister smallRNA dataset, suggesting that most satellite lncRNAs primarily serve as precursors for piRNA biogenesis. The two “stand-alone” lncRNAs were highly strand-biased, with 80% or more of the reads coming from one strand. We further investigated AAACTAC expression with RNA FISH and found the transcript specifically present in the oocyte nucleus is following a dynamic spatiotemporal pattern with highest expression in stage 3-5 oocytes. The transcription pattern of AAACTAC is conserved in the three other virilis clade species that contain this satellite DNA. However, we found expression of unrelated satellites in more distantly related D. borealis and littoralis both in the oocyte and the nurse cells. Overall, our work identifies a novel lncRNA AAACUAC transcript found in the oocyte nucleus during the generally transcriptionally silent karyosome stage, which conserved across ~5 MY of evolution, and is therefore a strong candidate for the discovery of novel functions of satellite lncRNAs in development.

Feline chronic enteropathy (FCE) is histologically most often diagnosed as lymphoplasmacytic enteritis (LPE) or lymphoma. Diagnosis relies heavily on the histopathological assessment of mucosal lymphocytes, yet such assessments show low reproducibility. This study explores whether intestinal biopsies from cats with FCE exhibit patterns of variation in mucosal lymphocyte counts, spatial distribution and nuclear morphology, and how these patterns relate to conventional diagnostic categories. We applied supervised deep learning to detect lymphocytes and map them to mucosal compartments using 1,121 whole slide images from 765 biopsy submissions, spanning 25 years and 11 institutions. For each fragment, we extracted quantitative features describing mucosal lymphocyte count, spacing and nuclear size. We then compared these features with pathologist grades, performed k-means clustering on principal components, and compared alignment of clusters with pathologic diagnoses. Quantitative features correlated variably with pathologists’ grades suggesting they capture some of the histologic variation recognized by experts. Unsupervised clustering of mucosal fragments did not reveal clearly separable groups. However, fragments with similar histologic appearance clustered together, suggesting that the method captured morphologic patterns. Clusters only showed partial alignment with pathologic diagnoses consistent with histological overlap between categories, variability in diagnostic interpretation and variation introduced by analytical noise. These findings support FCE as a histologic continuum and demonstrate that data-driven quantitative pathology can capture patterns of histological variation not fully reflected in conventional diagnostic categories. Together, they suggest limitations of the traditional enteritis–lymphoma dichotomy and subjective severity grading.

Cell-tracking is a powerful approach for interrogating disease processes, yet the lack of longitudinal probes limits our ability to study chronic disease progression. Here, we put forth AmmonChrom7 as a fluorescence probe that enables bright long-lasting cellular labeling for high-sensitivity, high-resolution tracking in vivo. Following intravenous and intratracheal transplantation of AmmonChrom7-labeled macrophages, we map the distribution of systemic myeloid and alveolar macrophage populations across organs over months, revealing transient trafficking dynamics and long-term retention sites. Moreover, by correlating in vivo and ex vivo organ fluorescence, we demonstrate the feasibility of non-invasive quantification of cell migration using AmmonChrom7. Finally, we demonstrate the utility of this platform for visualizing disease pathophysiology in the context of metabolic dysfunction-associated liver disease, where both intravenously and intratracheally transplanted macrophages exhibited increased accumulation in the liver over months, consistent with spleen- and lung-derived recruitment.

Forward genetic screening links genotype to phenotype by introducing random genetic perturbations and identifying phenotype-altering mutations. Although genome-scale screens are routine for simple phenotypes in cultured cells, extending them to complex image-based phenotypes remains challenging. Here we present SPARCS, a microscopy-based platform for forward genetic screening on single-cell images. SPARCS physically isolates mutants in situ by automated laser microdissection, enabling image-based screening at unprecedented scale with multimodal hit phenotyping. We demonstrate SPARCS in genome-wide CRISPR knockout screens of autophagosome formation and activation of the immune sensor STING across 70 million cells. Via machine learning-based image analysis, SPARCS recovered most macroautophagy genes and identified GPHR as a pH-dependent regulator of STING. Mass spectrometry-based proteomics of isolated hit cells revealed ER/Golgi disruption and nominated additional STING regulators via in silico perturbation modeling. These results establish SPARCS as a scalable platform for genome-wide genetic screening of complex cellular phenotypes with a proteome-level readout.