The safety of recombinant human hyaluronidase PH20 in nonclinical models: An overview of toxicology, pharmacology, and impact of anti-PH20 antibodies.

Hyaluronan (HA) is a glycosaminoglycan that forms a gel-like barrier in the subcutaneous (SC) space, limiting bulk fluid flow and the dispersion of SC-administered therapeutics. Recombinant human hyaluronidase PH20 (rHuPH20) facilitates the rapid delivery of co-administered therapeutics by depolymerizing HA in the SC space. Administration of rHuPH20 can induce the formation of anti-rHuPH20 antibodies, or anti-drug antibodies (ADAs), with the potential to bind endogenous PH20 hyaluronidase in the adult testes and epididymis. Using a variety of relevant animal models and multiple dose regimens of rHuPH20 across the full spectrum of animal development, we demonstrated that rHuPH20 administration resulted in the formation of ADAs. Although these ADAs can bind both the recombinant rHuPH20 enzyme and recombinant versions of animal model-specific hyaluronidases, they had no impact on fertility parameters (as measured by sperm concentration and motility, litter size, and litter viability) or fetal development. We present the result of our nonclinical studies in order of the developmental lifecycle, beginning with adults. Toxicology studies that extend beyond the standard package are also presented. These studies demonstrate the favorable safety profile of rHuPH20 and ADAs in nonclinical models. Additionally, we identified substantial safety margins for therapeutically relevant doses of rHuPH20.

Heparin Induced Thrombocytopenia Testing.

This article provides a comprehensive overview of Heparin-Induced Thrombocytopenia (HIT) with an emphasis on laboratory testing and advantages of automation. HIT is a critical condition arising from heparin exposure, leading to a contradictory combination of thrombocytopenia with an increased thrombosis risk. The article discusses HIT's history, clinical presentation, laboratory diagnosis, and management strategies. It highlights the importance of interdisciplinary collaboration for effective diagnosis and treatment, underscoring advancements in technology and targeted therapies that are shaping future approaches to HIT management.

A new era of synthetic biology-microbial community design.

Synthetic biology conceptualizes biological complexity as a network of biological parts, devices, and systems with predetermined functionalities and has had a revolutionary impact on fundamental and applied research. With the unprecedented ability to synthesize and transfer any DNA and RNA across organisms, the scope of synthetic biology is expanding and being recreated in previously unimaginable ways. The field has matured to a level where highly complex networks, such as artificial communities of synthetic organisms, can be constructed. In parallel, computational biology became an integral part of biological studies, with computational models aiding the unravelling of the escalating complexity and emerging properties of biological phenomena. However, there is still a vast untapped potential for the complete integration of modelling into the synthetic design process, presenting exciting opportunities for scientific advancements. Here, we first highlight the most recent advances in computer-aided design of microbial communities. Next, we propose that such a design can benefit from an organism-free modular modelling approach that places its emphasis on modules of organismal function towards the design of multispecies communities. We argue for a shift in perspective from single organism-centred approaches to emphasizing the functional contributions of organisms within the community. By assembling synthetic biological systems using modular computational models with mathematical descriptions of parts and circuits, we can tailor organisms to fulfil specific functional roles within the community. This approach aligns with synthetic biology strategies and presents exciting possibilities for the design of artificial communities. Graphical Abstract.

Discriminatory properties of QALY-based CEA for patients with disabilities: A Duchenne muscular dystrophy case study.

OBJECTIVE: Quality-adjusted life years (QALYs) have been challenged as a measure of benefit for people with disabilities, particularly for those in low utility health states or with irreversible disability. This study examined the impact of a QALY-based assessment on the price for a hypothetical treatment for Duchenne muscular dystrophy (DMD), a progressive, genetic neuromuscular disease. METHODS: A previously published, five-state model, which analyzed treatments for early ambulatory (EA) DMD patients, was replicated, validated, and adapted to include early non-ambulatory (ENA) DMD patients. The model was used to assess a QALY-based threshold price (maximum cost-effective price) for a hypothetical treatment for 13-year-old ENA and 5-year-old EA patients (initial health states with lower and higher utility, respectively). All inputs were replicated including willingness-to-pay (WTP) thresholds of $50,000 to $200,000/QALY. RESULTS: In contrast to EA patients, ENA patients had a 98% modeled decline in QALY-based threshold price at a WTP of $150,000/QALY or higher, despite equal treatment benefit (delayed progression/death). At $100,000/QALY or lower, net non-treatment costs exceeded health benefits, implying any treatment for ENA patients would not be considered cost-effective, even at $0 price, including an indefinite pause in disease progression. CONCLUSIONS: For certain severe, disabling conditions, traditional approaches are likely to conclude that treatments are not cost-effective at any price once a patient progresses to a disabled health state with low utility value. These findings elucidate theoretical/ethical concerns regarding potential discriminatory properties of traditional QALY assessments for people with disabilities, particularly those who have lost ambulation or have other physical limitations.

Application of the updated International IgA Nephropathy Prediction Tool in children one- or two-years post-biopsy.

The pediatric International IgA Nephropathy (IgAN) Prediction Tool comprises two models with and without ethnicity and is the first method to predict the risk of a 30% decline in estimated glomerular filtration rate (eGFR) or kidney failure in children at the time of biopsy using clinical risk factors and Oxford MEST histology scores. However, it is unknown if the Prediction Tool can be applied after a period of observation post-biopsy. Using an international multi-ethnic cohort of 947 children with IgAN, 38% of whom were followed into adulthood, the Prediction Tool was updated for use one-year after biopsy. Compared to the original pediatric Prediction Tool, the updated post-biopsy Prediction Tool had a better model fit with higher R2D (51%/50% vs 20%), significant increase in 4-year C-statistics (0.83 vs 0.73/0.69, ΔC 0.09 [95% confidence interval 0.07-0.10] and ΔC 0.14 [0.12-0.15]) and better 4-year calibration with lower integrated calibration indices (0.74/0.54 vs 2.45/1.01). Results were similar after internal validation and when the models were applied two-years after biopsy. Trajectories of eGFR after a baseline one-year post-biopsy were non-linear and those at higher predicted risk started with a lower eGFR and experienced a more rapid decline over time. In children, eGFR had a variable rate of increase until 15-18 years old then decreased linearly with a more rapid decline in higher risk groups that was similar to young adults of comparable risk. Thus, the original pediatric Prediction Tool should be used in children at the time of biopsy, and the updated pediatric Prediction Tool should be used to revaluate risk one- or two-years after biopsy.

Clinical, Imaging, and Technical Factors Associated with Successful Genomic Profiling of Bone Biopsy Tissue in Prostate Cancer.

BACKGROUND AND OBJECTIVE: The source of tissue for genomic profiling of metastatic castration-resistant prostate cancer (mCRPC) is often limited to osseous metastases. To guide patient management, metastatic site selection and the technique for targeted bone biopsies are critical for identifying deleterious gene mutations. Our objective was to identify key parameters associated with successful large-panel DNA sequencing. METHODS: We analyzed parameters for 243 men with progressing mCRPC who underwent 269 bone biopsies for genomic profiling between 2014 and 2018. Univariate and multivariate analyses were performed for clinical, imaging (bone scan; fluorodeoxyglucose [FDG] positron emission tomography [PET]; computed tomography [CT]; magnetic resonance imaging), and technical (biopsy site, number of samples, needle gauge) features associated with successful genomic profiling. KEY FINDINGS AND LIMITATIONS: Overall, 159 of 269 biopsies (59%) generated sufficient tumor material for a genomic profile. Seventy (26%) of the failures were histopathologically negative for mCRPC and 40 (15%) had insufficient tumor for genomic profiling. Of 199 mCRPC samples submitted for molecular testing, 159 (80%) yielded a genomic profile. On univariate analysis, PSA, serum acid phosphatase, number of biopsy samples, FDG PET positivity, CT attenuation, and CT morphology were significantly associated with genomic profiling success. On multivariate analysis, higher FDG maximum standardized uptake value (odds ratio [OR] 7.51, 95% confidence interval [CI] 3.01-18.78; p < 0.001), higher number of biopsy samples (OR 4.73, 95% CI 1.49-15.02; p = 0.008), and lower mean CT attenuation (OR 0.4, 95% CI 0.18-0.89; p = 0.025) were significantly associated with sequencing success. CONCLUSIONS AND CLINICAL IMPLICATIONS: In patients with mCRPC, bone biopsies from sites with metabolic activity and lower CT attenuation are associated with higher success rates for genomic profiling via a large-panel DNA sequencing platform. PATIENT SUMMARY: We identified factors associated with successful genetic testing of bone tissue for patients with metastatic prostate cancer. Our findings may help in guiding the right scan technique and biopsy site for personalized treatment planning.

In silico approaches to study the human asparagine synthetase: An insight of the interaction between the enzyme active sites and its substrates.

Cancer is a leading concern and important cause of death worldwide. Cancer is a non-communicable illness defined as uncontrolled division of cells. It can develop into metastatic cancer when tumor cells migrate to other organs. In recent years evidence has emerged that the bioavailability of Asn play a crucial role in cancer metastasis. Asn is a non-essential amino acid formed from an ATP dependent catalyzed reaction by the enzyme asparagine synthetase (ASNS), where Asp and Gln are converted to Asn and Glu, respectively. The human ASNS enzyme consist of 561 amino acids, with a molecular weight of 64 KDa. ASNS governs the activation of transcriptional factors that regulate the process of metastasis. In this work the 3D model of ASNS in E. coli (AS-B) and the human ASNS docked with its different ligands have been used to study the 3D mechanism of the conversion of Asp and Gln to Asn and Glu, in human ASNS. The stability evaluation of the docked complexes was checked by molecular dynamic simulation through the bioinformatic tool Desmond. The binding residues and their interactions can be exploited for the development of inhibitors, as well as for finding new drug molecules against ASNS and prevention of metastatic cancer.

Enolase inhibitors as therapeutic leads for Naegleria fowleri infection.

Infections with the pathogenic free-living amoebae Naegleria fowleri can lead to life-threatening illnesses including catastrophic primary amoebic meningoencephalitis (PAM). Efficacious treatment options for these infections are lacking and the mortality rate remains >95% in the US. Glycolysis is very important for the infectious trophozoite lifecycle stage and inhibitors of glucose metabolism have been found to be toxic to the pathogen. Recently, human enolase 2 (ENO2) phosphonate inhibitors have been developed as lead agents to treat glioblastoma multiforme (GBM). These compounds, which cure GBM in a rodent model, are well-tolerated in mammals because enolase 1 (ENO1) is the predominant isoform used systemically. Here, we describe findings that demonstrate these agents are potent inhibitors of N. fowleri ENO (NfENO) and are lethal to amoebae. In particular, (1-hydroxy-2-oxopiperidin-3-yl) phosphonic acid (HEX) was a potent enzyme inhibitor (IC50 = 0.14 ± 0.04 µM) that was toxic to trophozoites (EC50 = 0.21 ± 0.02 µM) while the reported CC50 was >300 µM. Molecular docking simulation revealed that HEX binds strongly to the active site of NfENO with a binding affinity of -8.6 kcal/mol. Metabolomic studies of parasites treated with HEX revealed a 4.5 to 78-fold accumulation of glycolytic intermediates upstream of NfENO. Last, nasal instillation of HEX increased longevity of amoebae-infected rodents. Two days after infection, animals were treated for 10 days with 3 mg/kg HEX, followed by one week of observation. At the end of the one-week observation, eight of 12 HEX-treated animals remained alive (resulting in an indeterminable median survival time) while one of 12 vehicle-treated rodents remained, yielding a median survival time of 10.9 days. However, intranasal HEX delivery was not curative as brains of six of the eight survivors were positive for amoebae. These findings suggest that HEX requires further evaluation to develop as a lead for treatment of PAM.

Safety Assessment of Methylxanthines as Used in Cosmetics.

The Expert Panel for Cosmetic Ingredient Safety (Panel) assessed the safety of three methylxanthines, Caffeine, Theobromine, and Theophylline, as used in cosmetics. All of these ingredients are reported to function as skin-conditioning agents in cosmetic products. The Panel reviewed the data relevant to the safety of these ingredients and concluded that Caffeine, Theobromine, and Theophylline are safe in cosmetics in the present practices of use and concentration described in this safety assessment.

Monovalent Pseudo-Natural Product Degraders Supercharge the Native Degradation of IDO1 by KLHDC3.

Targeted protein degradation (TPD) modulates protein function beyond inhibition of enzyme activity or protein-protein interactions. Most degraders function by proximity induction, and directly bridge an E3 ligase with the target to be degraded. However, many proteins might not be addressable via proximity-based degraders, and other challenges, such as resistance acquisition, exist. Here, we identified pseudo-natural products derived from (-)-myrtanol, termed iDegs, that inhibit and induce degradation of the immunomodulatory enzyme indoleamine-2,3-dioxygenase 1 (IDO1) by a distinct mechanism. iDegs induce a unique conformational change and, thereby, boost IDO1 ubiquitination and degradation by the cullin-RING E3 ligase CRL2KLHDC3, which we identified to also mediate native IDO1 degradation. Therefore, iDegs supercharge the native proteolytic pathway of IDO1, rendering this mechanism of action distinct from traditional degrader approaches involving proteolysis-targeting chimeras (PROTACs) or molecular-glue degraders (MGDs). In contrast to clinically explored IDO1 inhibitors, iDegs reduce formation of kynurenine by both inhibition and induced degradation of the enzyme and should also modulate non-enzymatic functions of IDO1. This unique mechanism of action may open up new therapeutic opportunities for the treatment of cancer beyond classical inhibition of IDO1.

Real-world effectiveness of sotrovimab in preventing hospitalization and mortality in high-risk patients with COVID-19 in the United States: A cohort study from the Mayo Clinic electronic health records.

BACKGROUND: To describe outcomes of high-risk patients with coronavirus disease 2019 (COVID-19) treated with sotrovimab, other monoclonal antibodies (mAbs), or antivirals, and patients who did not receive early COVID-19 treatment. We also evaluate the comparative effectiveness of sotrovimab versus no treatment in preventing severe clinical outcomes. METHODS: This observational retrospective cohort study analyzed Mayo Clinic electronic health records. Non-hospitalized adult patients diagnosed with COVID-19 from May 26, 2021 and April 23, 2022 and at high risk of COVID-19 progression were eligible. The primary outcome was 29-day all-cause hospitalization and/or death. Outcomes were described for patients treated with sotrovimab, other mAbs, or antivirals, and eligible but untreated patients, and compared between sotrovimab-treated and propensity score (PS)-matched untreated cohorts. RESULTS: We included 35,485 patients (sotrovimab, 1369; other mAbs, 6488; antivirals, 133; high-risk untreated, 27,495). A low proportion of patients treated with sotrovimab (n = 33/1369, 2.4%), other mAbs (n = 147/6488, 2.3%), or antivirals (n = 2/133, 1.5%) experienced all-cause hospitalization or death. Among high-risk untreated patients, the percentage of all-cause hospitalization or death was 3.3% (n = 910/27,495). In the PS-matched analysis, 2.5% (n = 21/854) of sotrovimab-treated patients experienced all-cause hospitalization and/or death versus 2.8% (n = 48/1708) of untreated patients (difference, -0.4%; p = 0.66). Significantly fewer sotrovimab-treated patients required intensive care unit admission (0.5% vs 1.8%; difference, -1.3%; p = 0.002) or respiratory support (3.5% vs 8.7%; difference, -5.2%; p < 0.001). CONCLUSIONS: There was no significant difference in the proportion of sotrovimab-treated and PS-matched untreated patients experiencing 29-day all-cause hospitalization or mortality, although significantly fewer sotrovimab-treated patients required intensive care unit admission or respiratory support.

Genome Report: Pseudomolecule-scale genome assemblies of Drepanocaryum sewerzowii and Marmoritis complanata.

The Nepetoideae, a subfamily of Lamiaceae (mint family), is rich in aromatic plants, many of which are sought after for their use as flavours and fragrances or for their medicinal properties. Here we present genome assemblies for two species in Nepetiodeae: Drepanocaruym sewerzowii and Marmoritis complanata. Both assemblies were generated using Oxford Nanopore Q20+ reads with contigs anchored to nine pseudomolecules that resulted in 335 Mb and 305 Mb assemblies, respectively, and BUSCO scores above 95% for both the assembly and annotation. We furthermore provide a species tree for the Lamiaceae using only genome derived gene models, complementing existing transcriptome and marker-based phylogenies.

Ketone body metabolism and the NLRP3 inflammasome in Alzheimer's disease.

Alzheimer's disease (AD) is a degenerative brain disorder and the most common form of dementia. AD pathology is characterized by senile plaques and neurofibrillary tangles (NFTs) composed of amyloid-ß (Aß) and hyperphosphorylated tau, respectively. Neuroinflammation has been shown to drive Aß and tau pathology, with evidence suggesting the nod-like receptor family pyrin domain containing 3 (NLRP3) inflammasome as a key pathway in AD pathogenesis. NLRP3 inflammasome activation in microglia, the primary immune effector cells of the brain, results in caspase-1 activation and secretion of IL-1ß and IL-18. Recent studies have demonstrated a dramatic interplay between the metabolic state and effector functions of immune cells. Microglial metabolism in AD is of particular interest, as ketone bodies (acetone, acetoacetate (AcAc), and ß-hydroxybutyrate (BHB)) serve as an alternative energy source when glucose utilization is compromised in the brain of patients with AD. Furthermore, reduced cerebral glucose metabolism concomitant with increased BHB levels has been demonstrated to inhibit NLRP3 inflammasome activation. Here, we review the role of the NLRP3 inflammasome and microglial ketone body metabolism in AD pathogenesis. We also highlight NLRP3 inflammasome inhibition by several ketone body therapies as a promising new treatment strategy for AD.

Distinct plasma metabolomic signatures differentiate autoimmune encephalitis from drug-resistant epilepsy.

OBJECTIVE: Differentiating forms of autoimmune encephalitis (AE) from other causes of seizures helps expedite immunotherapies in AE patients and informs studies regarding their contrasting pathophysiology. We aimed to investigate whether and how Nuclear Magnetic Resonance (NMR)-based metabolomics could differentiate AE from drug-resistant epilepsy (DRE), and stratify AE subtypes. METHODS: This study recruited 238 patients: 162 with DRE and 76 AE, including 27 with contactin-associated protein-like 2 (CASPR2), 29 with leucine-rich glioma inactivated 1 (LGI1) and 20 with N-methyl-d-aspartate receptor (NMDAR) antibodies. Plasma samples across the groups were analyzed using NMR spectroscopy and compared with multivariate statistical techniques, such as orthogonal partial least squares discriminant analysis (OPLS-DA). RESULTS: The OPLS-DA model successfully distinguished AE from DRE patients with a high predictive accuracy of 87.0 ± 3.1% (87.9 ± 3.4% sensitivity and 86.3 ± 3.6% specificity). Further, pairwise OPLS-DA models were able to stratify the three AE subtypes. Plasma metabolomic signatures of AE included decreased high-density lipoprotein (HDL, -(CH2)n-, -CH3), phosphatidylcholine and albumin (lysyl moiety). AE subtype-specific metabolomic signatures were also observed, with increased lactate in CASPR2, increased lactate, glucose, and decreased unsaturated fatty acids (UFA, -CH2CH=) in LGI1, and increased glycoprotein A (GlycA) in NMDAR-antibody patients. INTERPRETATION: This study presents the first non-antibody-based biomarker for differentiating DRE, AE and AE subtypes. These metabolomics signatures underscore the potential relevance of lipid metabolism and glucose regulation in these neurological disorders, offering a promising adjunct to facilitate the diagnosis and therapeutics.

MAGPIE: An interactive tool for visualizing and analyzing protein-ligand interactions.

Quantitative tools to compile and analyze biomolecular interactions among chemically diverse binding partners would improve therapeutic design and aid in studying molecular evolution. Here we present Mapping Areas of Genetic Parsimony In Epitopes (MAGPIE), a publicly available software package for simultaneously visualizing and analyzing thousands of interactions between a single protein or small molecule ligand (the "target") and all of its protein binding partners ("binders"). MAGPIE generates an interactive three-dimensional visualization from a set of protein complex structures that share the target ligand, as well as sequence logo-style amino acid frequency graphs that show all the amino acids from the set of protein binders that interact with user-defined target ligand positions or chemical groups. MAGPIE highlights all the salt bridge and hydrogen bond interactions made by the target in the visualization and as separate amino acid frequency graphs. Finally, MAGPIE collates the most common target-binder interactions as a list of "hotspots," which can be used to analyze trends or guide the de novo design of protein binders. As an example of the utility of the program, we used MAGPIE to probe how different antibody fragments bind a viral antigen; how a common metabolite binds diverse protein partners; and how two ligands bind orthologs of a well-conserved glycolytic enzyme for a detailed understanding of evolutionarily conserved interactions involved in its activation and inhibition. MAGPIE is implemented in Python 3 and freely available at https://github.com/glasgowlab/MAGPIE, along with sample datasets, usage examples, and helper scripts to prepare input structures.

Simultaneous time-resolved inorganic haloamine measurements enable analysis of disinfectant degradation kinetics and by-product formation.

We demonstrate the application of proton transfer time-of-flight mass spectrometry (PTR-TOF-MS) in monitoring the kinetics of disinfectant decay in water with a sensitivity one to three orders of magnitude greater than other analytical methods. Chemical disinfection inactivates pathogens during water treatment and prevents regrowth as water is conveyed in distribution system pipes, but it also causes formation of toxic disinfection by-products. Analytical limits have hindered kinetic models, which aid in ensuring water quality and protecting public health by predicting disinfection by-products formation. PTR-TOF-MS, designed for measuring gas phase concentrations of organic compounds, was able to simultaneously monitor aqueous concentrations of five inorganic haloamines relevant to chloramine disinfection under drinking water relevant concentrations. This novel application to aqueous analytes opens a new range of applications for PTR-TOF-MS.

Impacts of stream drying depend on stream network size and location of drying.

Stream drying is increasing globally, with widespread impacts on stream ecosystems. Here, we investigated how the impacts of drying on stream ecosystem connectivity might depend on stream network size and the location of drying within the stream network. Using 11 stream networks from across the United States, we simulated drying scenarios in which we varied the location and spatial extent of drying. We found that the rate of connectivity loss varied with stream network size, such that larger stream networks lost connectivity more rapidly than smaller stream networks. We also found that the rate of connectivity loss varied with the location of drying. When drying occurred in the mainstem, even small amounts of drying resulted in rapid losses in ecosystem connectivity. When drying occurred in headwater reaches, small amounts of drying had little impact on connectivity. Beyond a certain threshold, however, connectivity declined rapidly with further increases in drying. Given the increasing stream drying worldwide, our findings underscore the need for managers to be particularly vigilant about fragmentation when managing at large spatial scales and when stream drying occurs in mainstem reaches.

Prediction of the mechanism of suicide among Minnesota residents using data from the Minnesota violent death reporting system (MNVDRS).

BACKGROUND: Suicide remains a major public health problem, and firearms are used in approximately half of all such incidents. This study sought to predict the occurrence of suicide specifically by firearm, as opposed to any other means of suicide, in order to help inform possible life-saving interventions. METHODS: This study involved data from the Minnesota Violent Death Reporting System. Models evaluated whether data beyond basic demographics generated increased prediction accuracy. Models were built using random forests, logistic regression and data imputation. Models were evaluated for prediction accuracy using the area under the curve analysis and for proper calibration. RESULTS: Results showed that models constructed with social determinants and personal history data led to increased prediction accuracy in comparison to models constructed with basic demographic information only. The study identified an optimised 'top 20' variables model with a 73% chance of correctly discerning relative incident risk for a pair of individuals. Age, height/weight, employment industry/occupation, sex and education level were found to be most highly predictive of firearm suicide in the study's 'top 20' model. CONCLUSIONS: The study demonstrated that the use of a firearm in a death by suicide, as opposed to any other means of suicide, can be reasonably well predicted when an individual's social determinants and personal history are considered. These predictive models could help inform many prevention strategies, such as safe storage practices, background checks for firearm purchases or red flag laws.

FIND-seq: high-throughput nucleic acid cytometry for rare single-cell transcriptomics.

Rare cells have an important role in development and disease, and methods for isolating and studying cell subsets are therefore an essential part of biology research. Such methods traditionally rely on labeled antibodies targeted to cell surface proteins, but large public databases and sophisticated computational approaches increasingly define cell subsets on the basis of genomic, epigenomic and transcriptomic sequencing data. Methods for isolating cells on the basis of nucleic acid sequences powerfully complement these approaches by providing experimental access to cell subsets discovered in cell atlases, as well as those that cannot be otherwise isolated, including cells infected with pathogens, with specific DNA mutations or with unique transcriptional or splicing signatures. We recently developed a nucleic acid cytometry platform called 'focused interrogation of cells by nucleic acid detection and sequencing' (FIND-seq), capable of isolating rare cells on the basis of RNA or DNA markers, followed by bulk or single-cell transcriptomic analysis. This platform has previously been used to characterize the splicing-dependent activation of the transcription factor XBP1 in astrocytes and HIV persistence in memory CD4 T cells from people on long-term antiretroviral therapy. Here, we outline the molecular and microfluidic steps involved in performing FIND-seq, including protocol updates that allow detection and whole transcriptome sequencing of rare HIV-infected cells that harbor genetically intact virus genomes. FIND-seq requires knowledge of microfluidics, optics and molecular biology. We expect that FIND-seq, and this comprehensive protocol, will enable mechanistic studies of rare HIV+ cells, as well as other cell subsets that were previously difficult to recover and sequence.