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RNA profiling technologies at single-cell resolutions, including single-cell and single-nuclei RNA sequencing (scRNA-Seq and snRNA-Seq, scnRNA-Seq for short), can help characterize the composition of tissues and reveal cells that influence key functions in both healthy and disease tissues. However, the use of these technologies is operationally challenging because of high costs and stringent sample-collection requirements. Computational deconvolution methods that infer the composition of bulk-profiled samples using scnRNA-Seq-characterized cell types can broaden scnRNA-Seq applications, but their effectiveness remains controversial. We produced the first systematic evaluation of deconvolution methods on datasets with either known or scnRNA-Seq-estimated compositions. Our analyses revealed biases that are common to scnRNA-Seq 10X Genomics assays and illustrated the importance of accurate and properly controlled data preprocessing and method selection and optimization. Moreover, our results suggested that concurrent RNA-Seq and scnRNA-Seq profiles can help improve the accuracy of both scnRNA-Seq preprocessing and the deconvolution methods that employ them. Indeed, our proposed method, Single-cell RNA Quantity Informed Deconvolution (SQUID), combined RNA-Seq transformation and dampened weighted least-squares deconvolution approaches to consistently outperform other methods in predicting the composition of cell mixtures and tissue samples. Furthermore, our analysis suggested that only SQUID could identify outcomes-predictive cancer cell subclones in pediatric acute myeloid leukemia and neuroblastoma datasets, suggesting that deconvolution accuracy improvements are vital to enabling its applications in the life sciences.

Recurrence and drug resistance are major challenges in the treatment of acute myeloid leukemia (AML) that spur efforts to identify new clinical targets and active agents. STAT3 has emerged as a potential target in resistant AML, but inhibiting STAT3 function has proven challenging. This paper describes synthetic studies and biological assays for a naphthalene sulfonamide inhibitor class of molecules that inhibit G-CSF-induced STAT3 phosphorylation in cellulo and induce apoptosis in AML cells. We describe two different approaches to inhibitor design: First, variation of substituents on the naphthalene sulfonamide core allows improvements in anti-STAT activity and creates a more thorough understanding of anti-STAT SAR. Second, a novel approach involving hybrid sulfonamide–rhodium(II) conjugates tests our ability to use cooperative organic–inorganic binding for drug development, and to use SAR studies to inform metal conjugate design. Both approaches have produced compounds with improved binding potency. In vivo and in cellulo experiments further demonstrate that these approaches can also lead to improved activity in living cells, and that compound 3aa slows disease progression in a xenograft model of AML.

Nearly 40% of children with acute myeloid leukemia (AML) suffer relapse due to chemoresistance, often involving upregulation of the oncoprotein STAT3 (signal transducer and activator of transcription 3). In this paper, rhodium(II)-catalyzed, proximity-driven modification identifies the STAT3 coiled-coil domain (CCD) as a novel ligand-binding site, and we describe a new naphthalene sulfonamide inhibitor that targets the CCD, blocks STAT3 function, and halts its disease-promoting effects in vitro, in tumor growth models, and in a leukemia mouse model, validating this new therapeutic target for resistant AML.

Rhodium(II)–fluorophore conjugates have strong rhodium-based fluorescence quenching that can be harnessed to report on a conjugate's cellular uptake and the intracellular decomposition rate. Information gleened from this study allowed the design of an improved STAT3 metalloinhibitor.

Multiple stress factors in honey bees are causing loss of bee colonies worldwide. Several infectious agents of bees are believed to contribute to this problem. The mechanisms of honey bee immunity are not completely understood, in part due to limited information about the types and abundances of hemocytes that help bees resist disease. Our study utilized flow cytometry and microscopy to examine populations of hemolymph particulates in honey bees. We found bee hemolymph includes permeabilized cells, plasmatocytes, and acellular objects that resemble microparticles, listed in order of increasing abundance. The permeabilized cells and plasmatocytes showed unexpected differences with respect to properties of the plasma membrane and labeling with annexin V. Both permeabilized cells and plasmatocytes failed to show measurable mitochondrial membrane potential by flow cytometry using the JC-1 probe. Our results suggest hemolymph particulate populations are dynamic, revealing significant differences when comparing individual hive members, and when comparing colonies exposed to diverse conditions. Shifts in hemocyte populations in bees likely represent changing conditions or metabolic differences of colony members. A better understanding of hemocyte profiles may provide insight into physiological responses of honey bees to stress factors, some of which may be related to colony failure.