ABSTRACT Fungi are the subject of extensive research due to their significant impact on human life. However, the development of tools based on fluorescent proteins has been slow in these species. Trichoderma reesei , a filamentous fungus known for its high secretion capability, shows great potential for producing heterologous proteins. However, the limited range of available fluorescent proteins for T. reesei hinders progress in its biological study and industrial applications. The goal of this study is to expand the panel of fluorescent markers for T. reesei by testing six new fluorescent proteins. To enable accurate comparisons, the method previously designed for eGFP was optimized to insert coding genes as a single copy by replacing the native pTEF promoter with the Neurospora crassa equivalent. The benchmarking assay identified one yellow‐fluorescent protein and two green‐fluorescent proteins that outperformed eGFP for transformant screening applications and a red fluorescent protein whose screening performance is comparable to that of eGFP, which would be valuable for colabeling purposes. These results could not have been predicted from the values reported in the Fluorescent Protein Database and the relative excitation of each protein at 488 nm accounts for only part of the difference. Additionally, the data obtained on fluorescent proteins and the approach used may be beneficial for investigating other types of fungi.
Suspension flow cytometry enables high-throughput cellular profiling at the single cell level, but these data lack positional information. Conversely, tissue-based imaging cytometry techniques reveal a cell's location within the tissue architecture and can provide insight into cell biology. It would be especially valuable if data analysis tools could incorporate data from imaging and flow cytometry platforms to gain complementary strengths when quantifying features of cells and populations. We hypothesized that per-cell Marker Enrichment Modeling (MEM) might provide a way to register cells between flow and imaging cytometry analysis. Here, we developed the Velociraptor machine learning workflow for cross-platform cytometry analysis. Velociraptor begins with a graph-based implementation of MEM to calculate per-cell quantitative phenotype labels. With this information, Velociraptor can then quickly calculate similarity between each cell's phenotype and search terms describing established cell types, cells of interest, or cells observed in other samples. Velociraptor was effective in registering cells within and between cytometry platforms. Integrated identification of cell populations was tested in several challenges, including comparisons of high dimensional datasets from cancer and immunology. Tested instrument types included imaging mass cytometry (IMC), cyclic immunohistochemistry (cycIHC), suspension mass cytometry (CyTOF), and suspension spectral flow cytometry (SFC). Between IMC and CyTOF, a comparison across imaging and flow cytometry platforms that use the same mass tag probes, Velociraptor accurately identified and registered immune cell types (median F1-measure of 0.81). Between SFC and CyTOF, a comparison of two fundamentally different probe types-fluorophores and metal tags-in suspension flow cytometry, Velociraptor was even more accurate at identifying and registering cells (concordance correlation coefficient of 0.99). Velociraptor was especially useful in heterogeneous samples where individual cells diverged in phenotype from the bulk population. In IMC imaging of human breast cancer, a previously unappreciated tumor cell subset was revealed by Velociraptor, characterized as CD15+, and validated as spatially segregated to the tumor core. In both cycIHC (8-dimensional imaging) and IMC imaging (40-dimensional imaging), Velociraptor accurately identified macrophages using a single search label as input. Notably, Velociraptor worked effectively with both extremely rare and highly abundant cell types and with cell search labels calculated from data and theoretical labels based on literature and expertise. The Velociraptor algorithm is freely available at https://github.com/cytolab.
Peripheral blood mononuclear cells (PBMCs) are commonly used for immune profiling in clinical research, but their isolation and cryopreservation present logistical challenges, particularly in settings with limited access to research facilities. To address this, we implemented a whole blood fixation protocol to preserve surface protein epitopes and enable high-dimensional flow cytometry without the need for immediate sample processing. A 36-marker lymphoid and 35-marker myeloid spectral flow cytometry panels were developed and optimized to characterize diverse immune populations, including T cell subsets, B cells, NK cells, monocytes, dendritic cells, and granulocytes. Panel design focused on antibody clone compatibility with fixed antigens, minimizing non-specific binding, and reliably detecting chemokine receptors. Performance was evaluated by comparing fresh and fixed whole blood, revealing consistent identification of major immune lineages. Long-term stability was demonstrated by using cryopreserved fixed blood from a healthy donor over two and half years, with immune profiles remaining stable. These findings support the use of fixed whole blood for high-dimensional spectral cytometry and its utility in longitudinal clinical immune monitoring.
Routinely used statistical hypothesis tests are often inadequate for longitudinal flow cytometry data, necessitating more sophisticated modeling approaches. Analyzing dynamic biomarker expression across hematopoietic maturation presents several analytical challenges, notably nonlinearity, serial correlation between subsequent stages, and varying total cell counts. This study addresses these issues by comparing traditional stage-by-stage hypothesis tests with two marginal modeling approaches: Generalized least squares (GLS) and a generalized linear model fitted with generalized estimating equations (GEE-GLM). We analyzed cell fractions expressing the dynamic biomarkers Bcl-2 (anti-apoptotic marker) and Ki-67 (proliferation marker), as well as the Bcl-2:Ki-67 ratio, across 20 stages of erythropoietic maturation in bone marrow aspirates from 25 patients with myelodysplastic syndromes (MDS), 25 patients with acute myeloid leukemia (AML), and 50 nonmalignant controls. The GLS modeling approach accounts for nonlinearity and correlation between subsequent maturation stages, while GEE-GLM additionally accounts for varying total cell counts by assigning greater weight to observations with larger cell counts. Instead of repeated stage-by-stage testing, GLS and GEE-GLM model the average maturation profile across different sub-populations, enabling visualization and robust population-level statistical inference. We demonstrate that variations in the total number of cells influence the estimated population mean profiles and their confidence intervals. This effect is particularly pronounced during early maturation stages, where low Ki-67+ cell counts can lead to substantially different estimates and conclusions when using the GEE-GLM approach. Conversely, the GLS approach offers the flexibility to model the unbounded Bcl-2:Ki-67 ratio, revealing differences between AML and nonmalignant patients across all 20 stages. The computational workflow and scripts are provided to facilitate reproducibility and application to similar time-dependent flow cytometry datasets.
Although routinely detected in human blood and tissue samples, immune cell doublets are typically discarded during data acquisition or analysis, often dismissed as technical artifacts arising from ex vivo cell handling. However, emerging evidence suggests that these immune cell doublets reflect biologically meaningful interactions, offering critical insights into immune communication during health and disease. In this review, we evaluate the current evidence supporting the existence of biological immune cell doublets across various human tissues, highlighting their diverse phenotypes and functions that distinguish them from technical noise. We explore how existing single-cell datasets and analytical frameworks can be repurposed to study doublets, and we examine the primary challenge in the field, which is the lack of definitive markers to distinguish biological doublets from artifacts. Finally, we examine how novel technologies, namely high-throughput image-enabled sorting, are facilitating the next generation of research on this topic.
