TID1/RDH54 knockouts were less likely to become petite. Even for lines with the same petite status, however, we found no correlation in fitness between the two replicate transformations performed. These results pose a challenge for any study using transformation to measure the fitness effect of genetic differences among strains. By attempting to hold haplotypes constant, we introduced more mutations that overwhelmed our ability to measure fitness differences between the genetic states. In this study, we transformed over one hundred different lines of yeast, using two independent transformations, and found that this common laboratory procedure can cause large changes to the microbe studied. Our study provides a cautionary tale of the need to use multiple transformants in fitness assays.How the number of genome copies modifies the effect of random mutations remains poorly known. In yeast, researchers have investigated these effects for knock-out or other large-effect mutations, but have not accounted for differences at the mating-type locus. We set out to compare fitness differences among strains that differ in ploidy and/or zygosity using a panel of spontaneously arising mutations acquired in haploid yeast from a previous study. To ensure no genetic differences, even at the mating-type locus, we embarked on a series of transformations, which first sterilized and then temporarily introduced plasmid-borne mating types. Despite these attempts to equalize the haplotypes, fitness variation introduced during transformation swamped the differences among the original mutation-accumulation lines. While colony size looked normal, we observed a bi-modality in the maximum growth rate of our transformed yeast and determined that many of the slow growing lines were respiratory deficient (“petite”). Not previously reported, we found that yeast that were  From the very first description of mutations in the form of newly arisen polyploids in plants by https://open.library.ubc.ca/media/stream/pdf/24/1.0397308/3).It is standard practice in molecular biology to construct vectors carrying genes of interest and transforming these into bacteria or yeast . For traChara), others have two , or many (polyploid plants). Yet others exist as multicellular forms in both ploidy stages, called haploid-diploid or haplodiplontic . Even in multicellular diplont organisms such as humans, the difference in selection in the haploid and diploid cell are of great importance. For example, mutations that render fitness advantages in gametes may have unpredicted consequences for a developing zygote (Among the many distinguishing characteristics of life is the number of copies of an organism’s genome (“ploidy”). While many organisms spend the majority of their life with one copy of their genome  in yeast. Even when considering sets of yeast lines with the same petite status, we found aberrant growth patterns across the haploid, heterozygous, and homozygous states. This indicates that our experimental lines changed in more ways  than those easily scored by petiteness and that these changes have greater impacts on growth rate than the mutations in the MA lines. We conclude that the haploid, heterozygous, and homozygous genotypes of the MA lines have acquired new variation, preventing us from extracting any information on the dominance of the small-effect spontaneous mutations acquired during MA. This conclusion highlights the challenge of estimating the effects of spontaneous mutations across ploidy levels: methods designed to hold “all else equal” instead induce differences.https://github.com/LinneaSandell/schrodingers-yeast.git).The laboratory notebooks, raw data, and statistical analyses are available on the GitHub site associated with this manuscript (MATa/). Half of the lines were rdh54. The RDH54 gene is involved in recombinational repair . We amplified the TRP1 locus from the pFA6a-TRP1 plasmid  we chosSTE4 locus replaced by TRP1 were selected through growth on agar plates with synthetic complete medium lacking tryptophan (SC-Trp). A single colony from the SC-Trp plate was then streaked to single colonies again on SC-Trp. These colonies were used to verify the knockout through PCR (see laboratory notebooks on GitHub for gel pictures) and to proceed with strain creation.Transformation was conducted using 1-h heat shock at 42 °C. Yeast that were successfully transformed and had their MAT locus in our lines and replaced with the HIS3 gene . We amplified the HIS3 locus from the pFA6a-His3MX6 plasmid . A single colony from the SC-His plate was then streaked to single colonies again on SC-His. These colonies were used to verify the knockout through PCR (see laboratory notebooks on GitHub for gel pictures). Once confirmed, a single colony from the plate was inoculated into 2 mL of YPAD media, grown at 200 rpm at 30 °C to saturation, and frozen in 15HIS4 in the MA experiment (an A to T mutation in pos chrIII:67039 leading to a premature stop codon) failed to grow on the SC-His plate post transformation and was dropped from the experiment. The 132 lines that had both the MAT and STE4 locus deleted were given new identifiers as knockout (KO) lines, grown to saturation and frozen in 15Transformation was conducted using 1-h heat shock at 42 °C. Yeast that were successfully transformed and had their MAT and STE4 loci we ran growth rate assays for the lines before and after the knockout procedure. We grew both the original lines with their ancestors and the lines with the deletions. A total of 20 To control for off-target insertion during the knockout of the We used the spline fitting method to extract the maximum slope of the growth curve, as described by STE4 and MAT and flagged four that had a z-score below −2.5. The analysis presented exclude these lines.We computed the difference in mean growth rate of each line before and after knockout of STE4 and MAT knockout we fit a linear mixed effect model of maximum slope as a function of KO (wildtype or knockout of STE4 and MAT), MA (MA or control), RDH54 status (wildtype or deletion mutant), and initial OD. We included day and machine as random effects. Each line had a line identity number (line ID) from the previous mutation-accumulation experiment, which is used as a random effect, and is the same for the line before and after knockout. The presence of genetic variance in the data is verified by fitting a simpler model (excluding KO and MA) to each of four groups: the control lines and MA lines before and after knockout. The significance of the genetic variance in explaining the data is evaluated by analysis of variance (ANOVA) of the model including line ID and a model excluding it.To estimate the average effect of the STE4 and LEU2 loci, OLP004 contains the MATSTE4, and URA3 loci  and dataset 2 (DS2). In DS1, growth in diploids was compared to that of the haploid line after knockout. In DS2, each line was randomly chosen  to have undergone OLP003 transformation or OLP004 transformation to represent their haploid state. We sampled the chosen plate with a toothpick and streaked it out to single colony on YPAD twice, after which three colonies were patched on both YPAD and leucine (in the case of OLP003 transformant) or uracil (for OLP004 transformant) drop-out plates. The colonies that grew on YPAD but not leucine/uracil drop-out plates were understood to have lost the plasmid and inoculated in YPAD, grown for 2 days in a 30 °C shaking incubator and then frozen into aliquots with 15LEU3  failed to grow on the SC-Leu plate post transformation with OLP003. We suspect LEU2 inserted during the MAT locus deletion was insufficient to rescue this mutant. We chose the OLP004 transformant to represent this line’s haploid state. One line . After one night of growth 5 -1 until 10,000 events had been measured. The resulting files were exported and analyzed with the flowCore package in R (the raw data files as well as summary figures are available on GitHub). All lines had the expected ploidy level.We confirmed the ploidy of our lines by flow cytometry. We used a protocol modified from MAT and STE4 led to increases in growth rates of both control and MA lines , see Four MA lines fell 2.5 standard deviations below the average knockout effect, with large decreases in their growth rates post knockout . We exclude these lines in the comparison of lines before and after knockout. The knockout of P < 10-5 for MA lines, P = 0.073 for control lines). After knockout of the STE4 and MAT loci, we verified this result .We found a significant difference in growth rate among MA lines, but not control lines, before knockout , we generated diploids by combining two plasmid-transformed haploids, followed immediately by selection for plasmid loss. We found a large reduction in the average growth rate of these diploid lines, compared to the corresponding haploid lines, but the latter had not undergone transformation with OLP003 or OLP004. To determine if this plasmid transformation was the source of the large decreased fitness of the diploids, we re-transformed our lines, saving the haploid lines after transformation to compare the growth of these to the diploids created from them (DS2). On average, we found that the haploids had also decreased in growth rate compared to DS1. However, we also found very strong bimodality in our data, see RDH54 gene intact were more likely to become petite . We also found that the frequency of petites was significantly higher in the haploids that were transformed with OLP004 (carrying STE4 and MATURA3) compared to the haploids that were transformed with OLP003 (carrying STE4 and LEU2) (P < 10-5).To determine whether variation among lines might have been caused by loss of mitochondrial function we spotted our lines on YPG to test for respiratory competence. While the number of petites was low among haploids in DS1 (seven out of the 131 lines without plasmid transformation: four of these were the four lines we detected as outliers in our comparison of growth rates before and after knockout), we found a high prevalence in the diploids (169 out of 260 lines). In DS2, where haploids were isolated post plasmid transformation, we found about an equal proportion of petites in the haploid and diploid lines . After further analysis, we found that diploid lines that had the We found no significant correlation between the growth rate of lines in DS1 and DS2 with the same genotype and petite status . Hence, RDH54 status, MA status (whether MA or control), genotype, and initial OD as fixed effects. We also include an interaction between MA status and genotype. We control for day, machine , and haplotype effect  by including them as random effects in our model. Neither MA nor the interaction of MA and genotype is significant in DS1 . Haploid lines had a significantly higher growth rate than diploids in DS1 . In DS2 MA is also not significant, but the interaction between MA and homozygosity is , with homozygous MA lines having a higher average growth rate than homozygous control lines.The linear mixed effect models we fit to DS1 and DS2 have the same structure: maximum slope is fitted as a function of petite status, RDH54 status, MA status (whether MA or control), genotype, and initial OD). The model using line identity rather than haplotype is preferred in both DS1 , less attention has been paid to differences in the fitness effect of mutations across ploidy levels . Previous studies have been limited to single genes or considered only one aspect of the question . In this project, we set out to conduct a large-scale experiment to measure both dominance within diploids and differences between ploidy levels using 100 previously established haplotypes with fitness-affecting spontaneous mutations in yeast  we put our lines through a number of transformations. These transformations led to high rates of loss of respiratory function (petites), accompanied with low growth rates. The combination of heat shock  and serit stress . The lost stress . Becauset stress . Four int stress  for respt stress . Remarkat stress . This diMAT and STE4 we found four lines with severely reduced growth (4/131 = 3.05% so around 1.5% per transformation). Notably, the 6% observed in the YKO was from knockouts of 5,916 different genes while we did 123 independent knockouts of the same two genes. Other studies have argued that it might not be the transformation itself that is mutagenic, but rather, the effect of a gene knockout that favours compensatory mutations to arise . Our initial analysis of DS1, in which haploids were isolated prior to plasmid transformation, showed large differences in the growth rate of haploid and diploid lines. As our results of DS2 show, in which haploids were isolated post-transformation, this effect was driven largely by off-target effects of plasmid transformation. By attempting to hold “all else equal”, the method we used introduced variance greater than what we set out to measure. We designed our study with the explicit purpose of measuring small effects, which made the fitness effect of the transformation easy to spot. Even so, it required a close to zero growth rate of a particular line for us to consider the possibility of induced defects. Even within the subset of lines that did not have respiratory defects, we did not find a signal of the mutations we set out to measure. Even with competition experiments assayed with flowcytometric methods Measuring small mutational effects across ploidy and genotype levels presents a major challenge. A previous attempt to measure the dominance effects of mutations found that heterozygous mutants became homozygous during the fitness assay, literally changing as they were being measured . Similarrdh54rdh54 deletion mutants at stationary phase have also been shown to be tolerant to heat shock  led to large changes in fitness that cloud the signal of the mutations of interest and thus make comparisons across ploidy and genotype challenging.10.7717/peerj.16547/supp-1Supplemental Information 1Triangles signify the four lines whose change in growth rate fell more than −2.5 standard deviations below the mean and were excluded from the analysis. The white triangles and circles signify lines that were unable to grow with galactose as a carbon source. Colored points with grey error bars represent line means and standard error of the mean .Click here for additional data file.10.7717/peerj.16547/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.16547/supp-3Supplemental Information 3Click here for additional data file.


Mammal hybridization is a speciation mechanism and an evolutionary driver. Goat-sheep, especially buck-ewe hybrids, are very rare with only one case reported in 2016, which is the subject of the work presented here. Blood transcriptome analysis revealed that the hybrid largely deviated from imprinting schemes previously described in sheep and other mammals. Furthermore, transcriptome regulation seems to differ from the parent transcriptomes, which is most likely a product of partially incompatible imprinting mechanisms from two closely related species. To gain a deeper understanding of hybridization in mammals we re-analyzed the RNA sequencing data of the buck-ewe hybrid and its parents. We found parent-of-origin-specific expression of genes that functionally clustered, which we explain with the Dobzhansky–Muller incompatibility (DMI) model. According to the DMI model, proteins which interact have a high probability of being barrier loci and hence are prone to monoallelic expression. We discovered enrichment of genes uniquely expressed by the buck-ewe hybrid, which implicate that it suffered from an NF-κB lymphoproliferative autoimmune disorder. Similar findings were reported in the F1 generation of hybrid mice. We propose that hybridization of two related species may lead to an autoimmune phenotype, due to immunoglobulin incompatibilities and incomplete silencing of barrier loci. Hybridization in mammals, however, is a common phenomenon and occurs at so-called hybrid zones, where phylogenetically related species come into contact5. Most hybrid animals are sterile, which is caused by chromosomal incompatibilities of the parental genomes. This leads to failure of gamete formation during meiosis6, which is most likely caused by divergent evolution leading to different structural variation patterns between closely related species7. Copy number variants (CNVs) caused by deletions and insertions8 as well as inversions9 have been discussed to be causative. Other factors preventing gene flow and consequently speciation events are barrier loci10. In the special case of goat-sheep hybrid embryos, hemolytic disease caused by an immune reaction of the mother against fetal red blood cells has been identified to prevent the development of hybrid embryos11. In the rare case of successful conception by a hybrid, the resulting offspring is considered a new species12, which makes hybrid speciation a mechanism of evolution13. The study presented here focuses on the analysis of the blood transcriptomes of a geep and its parents. The geep under investigation was born near Göttingen  in March 2014 as the result of the mating between a buck and an ewe. The geep died in 2018 due to polyhydramnios during pregnancy. It was pregnant with two fetuses at advanced developmental stages. The cytogenetics of the geep revealed that it had an intermediate karyotype of 57 chromosomes, whereas the buck had 60 and the ewe 54 chromosomes14. An in-depth analysis of the blood transcriptomes of the geep and its parents revealed abnormal imprinting patterns, which we concluded is a compensation mechanism for disadvantageous alleles15. Since this study was conducted using reference genome versions, which are now outdated, we re-analyzed the transcriptomes of the geep and its parents and found strong evidence for an NF-κB (nuclear factor 'kappa-light-chain-enhancer' of activated B-cells) autoimmune lymphoproliferative disorder in the geep.Goat-sheep hybrids (geeps) are rare and most of the described cases were offspring of rams mating with does15 were re-analyzed to elucidate if the usage of the most recent reference genome assemblies of Ovis aries (O. aries) and Capra hircus (C. hircus) reveal new insights to the biology of a mammal hybrid. A prerequisite for the analysis of RNAseq data from a hybrid animal is species discrimination of sequencing reads. To achieve that, the state-of-the-art sequence alignment software HISAT216 was used to map the reads of all three individuals to the O. aries and C. hircus genomes. The program HyScore, which we developed in our previous study15, was applied to the HISAT2 output to assign RNAseq reads unambiguously to one reference genome. This led to an exon coverage of about 18–26 × in all three animals  value > 1 were considered expressed, which led to the discovery of 863 genes expressed in geep blood, which were assigned to the maternal genome and 1100 genes assigned to the paternal genome. In total 659 genes were expressed in the blood of the hybrid but in neither parent blood transcriptomes. These are referred to in the following text as “uniquely expresses geep genes”. Gene cluster enrichment analysis with clusterProfiler revealed a clear differentiation of gene clusters, depending on the parental origin of genes expressed in the blood of the geep . UBE2N, the gene with the highest expression among unique geep genes, was located at the core of the PPI network connected to multiple interaction partners. Furthermore, CD28, which was linked to autoimmunity in 64 peer-reviewed articles, was strongly interconnected in the PPI network.Protein–protein interaction (PPI) analysis of uniquely expressed geep genes assigned to immune system GO terms with String11. The cause of death of the geep was polyhydramnios, a condition often presenting with fetal hemolytic anemia19. In a recent review Collins et al. discussed the function of immunoglobulin genes in reproductive isolation20. The authors came to the conclusion that coordination of evolution between heavy- and light-chain genes is crucial to avoid autoimmunity in vertebrate hybrids, which could lead to incompatibilities between hybrids and their offspring. They base their assumptions on the Dobzhansky–Muller incompatibility (DMI) model, which assumes genes encoding proteins that interact, are potential barrier loci because the interaction of gene products between different species would be too low in the hybrid offspring22. Genes encoding the antibody repertoire of a population change rapidly under selection pressure, which explains their high level of incompatibility between closely related species. Hence, monoallelic expression of genes from one parental genome is a mechanism to overcome these barrier loci. We found evidence for this mechanism in our previous study, where we showed that advantageous alleles were expressed in a monoallelic fashion in the blood of the geep15. In the study presented here, we show a clear functional clustering of genes by parental origin, which provides further evidence for the DMI model and as a result a disturbed immune system in the geep. Our results suggest that genes involved in autoimmunity are upregulated in the blood of the buck-ewe hybrid under investigation. This might not only help to understand its unexpected death during pregnancy but may also provide important insight into the immune biology of a mammal hybrid and the consequences on individual fitness. We indeed found several links to hemolytic diseases just by looking at the genes which were uniquely expressed in the blood of the hybrid. BCL2, for instance, is a pharmacological target in autoimmune hemolytic anemia (AIHA)23. Furthermore, AIHA patients have a significantly higher frequency and absolute count of CD28 null T helper, which also negatively correlates to the hemoglobin levels24. FASLG was linked to the syndrome of hemolysis, elevated liver enzymes, and low platelets (HELLP)25. Pro-inflammatory properties in HELLP were also attributed to NOD226.One mechanism, which was proposed to explain the reproductive barrier between sheep and goats is hemolytic disease caused by an immune reaction of the mother against fetal red blood cells27 and defective antibody gene rearrangements were found in individuals suffering from autoimmune diseases, like systemic lupus28. Genes that have been linked to lymphocytic disorders by more than three studies were BCL2, FASLG, CD28, FADD, IL15, and BCL2L1. Another disease captured by text mining was systemic lupus. Symptomatic resemblance to systemic lupus in mice was described over 50 years ago in the F1 hybrids of New Zealand black (NZB) and New Zealand white (NZW) mice29. CD28 was connected to systemic lupus by three studies32 and FASLG32 as well as TRAF532 by two studies. While these lines of evidence put CD28, FASLG, and BCL2 into the main focus of an autoimmune phenotype in mammal hybrids, our data suggest that UBE2N plays a major role in regulation of the syndrome. With the highest expression among all uniquely expressed genes in the hybrid and its central position in the PPI network  tags target proteins, which ultimately results in the activation of the transcription factor NF-κB33, one of the top pathways identified by gene cluster enrichment analysis , an NF-κB signaling protein interaction cluster.While this evidence is rather descriptive, a text mining approach led to the identification of a significant number of genes linked to lymphoproliferative and other autoimmune disorders involving lymphocytes. Immunoglobulins are essential for the correct assembly of lymphocytesUBE2N expression, which in turn might lead to ubiquitination of TAB3, as outlined by Ruland et al.34, and ultimately to the release of NF-κB. NF-κB has been linked to various autoimmune diseases in humans . Among those is a condition termed RELA Haploinsufficiency, which is an autoimmune lymphoproliferative syndrome36. Cause of the disease is a de-novo heterozygous nonsense mutation in the RELA gene, which encodes the transcription factor p65. It is possible that genomic rearrangements in the buck-ewe hybrid due to chromosomal incompatibility of the parent genomes during meiosis6 led to a heterozygous deleterious mutation in RELA or one parental copy has been silenced. However, since the geep was evidently healthy , our findings provide rationale for the development of polyhydramnios during pregnancy: an autoimmune phenotype of the hybrid might have been sufficient to lead to an immune reaction against fetal red blood cells. Autoimmune diseases in humans increase miscarriage risks but also reduce female fertility . We strongly suggest, that this mechanism should be considered in future research focusing on the reproductive barrier between related species. This line of evidence will be subject of future studies, which will focus on the genome assembly of the buck-ewe hybrid, its parents and the fetus.In summary, the evidence presented here points towards an NF-κB autoimmune disorder involving the lymphatic system in the buck-ewe hybrid, which is most likely caused by the incomplete silencing of barrier loci and immunoglobulin incompatibilities. We assume that the disorder is induced by high O. aries and C. hircus genomes  using HiSat2 version 2.1.0 with default settings16. Splice sites were derived from the Gene transfer format (GTF) files. Mapped reads were filtered by determining the best alignment result using HyScore15. FPKM values were calculated with Cufflinks version 2.2.138. Genes with an FPKM value > 1 were considered expressed. The heatmap was constructed with the Enhanced Heat Map function from the R package gplots .Reads were aligned to the most recent 39. Gene symbols were converted to ensemble IDs with the clusterProfiler Biological Id Translator (bitr) using the org.Hs.eg.db database. GO term analyses were performed with enrichGO . KEGG pathway40 analysis was done with enrichKEGG . Plots were created with the dotplot function. Genes with an enrichment P value < 0.001 for immune system related GO biological processes terms were used for downstream analyses. PPI analysis and NCBI PubMed text mining were performed with STRING (version 11.0)17 using default settings.Gene cluster comparison and visualization was performed with the R package clusterProfiler (version 4.2.2)Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.


Stem cells typically reside in a specialized physical and biochemical environment that facilitates regulation of their behavior. For this reason, stem cells are ideally studied in contexts that maintain this precisely constructed microenvironment while still allowing for live imaging. Here, we describe a long-term organ culture and imaging strategy for hematopoiesis in flies that takes advantage of powerful genetic and transgenic tools available in this system. We find that fly blood progenitors undergo symmetric cell divisions and that their division is both linked to cell size and is spatially oriented. Using quantitative imaging to simultaneously track markers for stemness and differentiation in progenitors, we identify two types of differentiation that exhibit distinct kinetics. Moreover, we find that infection-induced activation of hematopoiesis occurs through modulation of the kinetics of cell differentiation. Overall, our results show that even subtle shifts in proliferation and differentiation kinetics can have large and aggregate effects to transform blood progenitors from a quiescent to an activated state. Because of their key role in tissue maintenance and the inherent risk associated with their unchecked proliferative capacity, stem cell behavior is tightly regulated . This reDrosophila provides a powerful and genetically tractable model to study hematopoiesis . They catre (PSC). Howevertre (PSC). Vertebrtre (PSC). Drosophgenitors . Neverthgenitors . For exagenitors . Anothergenitors .Drosophila larvae is the primary lobe of a specialized organ known as the LG . Upon cell division, two similar sized progenitors are produced and undergo rapid growth such that their combined size exceeds the size of the original mother cell 3 hr after division, making cell division a potential driver for LG growth (+ dome-MESO-GFP+), distal progenitors (only dome-MESO-GFP+) and intermediate progenitors  was on average 76.08 µm2, while for core progenitors (dome+ Tep4+) it was on average 63.81 µm2 , intermediate progenitors (dome+ Hml+), and mature blood cells (dome- Hml+). We observed a reduction in the number of dividing progenitor cells following infection  A key characteristic of linear differentiation trajectory is that the dome-MESO-GFP declines throughout the process. In contrast, in the later phases of the sigmoid differentiation trajectory, dome-MESO-GFP levels become stable , GFPLOWdsRedHIGH (most differentiated), GFPHIGHdsRedHIGH and GFPLOWdsRedLOW , while GFPLOWdsRedLOW decreased slightly (2337 cells in wt LGs and 1888 cells in LGs following infection) and there was a rise in the number of differentiated GFPLOWdsRedHIGH cells .This study did not generate new reagents.https://github.com/Tanentzapf-Lab/LiveImaging_HematopiesisKinetics_Infection_Ho_Carr; copy archived at All raw data reported in this paper is deposited in the Source Data files in this study. MATLAB scripts used for counting total number of cells in a LG are publicly available . All othDrosophila melanogaster stocks and crosses were maintained on standard cornmeal medium (recipe from Bloomington Drosophila Stock Center) in vials or bottles at 25℃. Blood progenitor drivers used were Tep4-Gal4 , Ubi-FUCCI (RRID: BDSC_55124), UAS-FUCCI (RRID: BDSC_55117).ep4-Gal4  and domeome-Gal4  (kind gier-dsRed  (kind gi-GFP.nls  (kind gigstD-GFP  (kind giESO-lacZ  (originasRed.nls  (kind gi-mCherry  (kind giAB_2336790) in the glass bottom mounting dishes . All the antibodies were diluted in 0.1% PTX. The following primary antibodies were used: mouse anti-phospho-Histone H3 antibody  and mouse anti-LacZ antibody . The secondary antibody donkey anti-mouse Cy5  was used.LGs were dissected in ice cold Phosphate Buffer Saline (PBS). The dissected LGs were fixed in 4% paraformaldehyde (PFA) for 15 min, washed with 0.1% PTX  three times (each 5 min), then blocked with 16% Normal Goat Serum  for 15 min followed by an overnight primary antibody incubation at 4 ℃. The samples were washed with 0.1% PTX three times (each 5 min) and then blocked with 16% Normal Goat Serum for 15 min. The LG samples were then incubated in appropriate secondary antibodies for 2 hr at room temperature, followed by washes with 0.1% PTX three times (each 5 min) and then mounted in VECTASHIELD with DAPI  in room temperature. The connection between CNS, ring gland, LG, and heart tube should be maintained during all steps from larval dissection, organ mounting and to confocal imaging. The dissected organs were then placed and mounted in the Schneider’s medium supplied with 15% FBS  and 0.2 mg/mL insulin (Sigma I0516) in a glass bottom dish. The medium was prepared fresh in 10 min prior to dissection in room temperature. The LG was mounted in such a manner to align the dorsal-ventral axis of the tissue with the z-axis of the confocal optical section. To stabilize the LG, the organs were covered with a 1% agar pad and spacers made from 1% agar were placed in between agar pad and glass bottom dish to shield the organs from mechanical force. Optimal moisture conditions during live imaging was maintained by the addition of 2 ml of the medium on top of the agar pad. All live imaging experiments were performed at 25οC in a microscope incubation chamber . LGs were imaged using an Olympus FV1000 inverted confocal microscopy with a numerical aperture 1.30 UPLFLN 40 X oil immersion lens. Imaging duration varied due to movement caused by occasional heart tube contractions (see Early third instar larvae (84 hr after egg laying [AEL]) were chosen for all long-term live imaging experiments, washed using PBS for three times, followed by a quick rinse with 70% ethanol, then washed again using PBS for three times. Organs including larval LGs, fat bodies, ring gland, central nervous system, and heart tube were dissected in ions see . The midions see , which iions see  and MATL4, 1.2 μl Alexa Fluor azide, and 50 μl 1xClick iT EdU buffer additive) for 30 min at room temperature in dark. After the incubation, the cocktail solution was removed and the LGs were washed twice using 16% Normal Goat Serum (each 10 minutes) and mounted in VECTASHIELD with DAPI in glass bottom dishes. The EdU signal from the LGs was imaged using a laser with the excitation wavelength at 488 nm. Number of EdU-positive cells per primary lobe were counted manually in Fiji using a Cell counter plugin.Click-iT EdU (5-ethynyl-2´-deoxyuridine) imaging kit  was used to perform cell proliferation assay. Larvae were washed using PBS three times, quickly rinsed with 70% ethanol, and then washed and dissected in the Schneider medium in room temperature. The LGs  were cultured in the Schneider medium (with 15% FBS and 0.2 mg/mL insulin) supplied with EdU solution with the final concentration of 10 μM for an hour. Following incubation in the EdU solution, the LGs were fixed in 4% PFA for 15 min, rinsed with 16% Normal Goat Serum twice, washed with 0.1% PTX for 20 min, and then incubated in a Click-iT reaction cocktail . The Sytox Green dye functions as an indicator of dying cells as the dye is impermeable to the plasma membrane of live cells. Dissected LGs were incubated and imaged in the Schneider medium containing 2 μM Sytox Green or PBS as a positive control. A stock solution of Sytox Green (5 mM in DMSO) was prepared and diluted in 1:2500 to a final concentration of 2 μM. The LG was imaged immediately after mounted in Sytox Green-containing medium. Cell death was assessed by counting all the progenitors shown in  or in thCdt2 while CycB is targeted by the APC/C for proteasomal degradation from mid-mitosis throughout G1 phase. By combining the two probes, cells that are in G1 phase are labelled in green (E2f1-GFP accumulation), in S phase are labelled in red (CycB-mRFP accumulation), and in G2 phase are labelled in yellow (presence of both E2f1-GFP and CycB-mRFP). The fluorescent intensities of GFP and RFP of individual cells in the LG in each time point were tracked and exported using the Fiji TrackMate plugin  from long-term LG videos was extracted using 2 MATLAB scripts and an image analysis workflow (+ dome+) by the total number of progenitors (dome+) in the LG. Number of pH3+ progenitors were counted in Fiji using the Cell Counter plugin. (10) The positions where blood progenitors divide inside a LG were recorded and the information from multiple long-term videos was then summarized in a heat map (as described above in the Heat map construction section) to visualize regions with different level of mitotic activities.Mitotic events were tracked in blood progenitors labelled by dome-Gal4 >UAS mGFP or dome-MESO-GFP in long-term LG videos using Fiji. The following quantitative analyses were performed on the mitotic events: (1) Duration of mitotic events was defined as the time a mother progenitor spent from the onset of mitosis throughout to the end where the nucleus of two progenies reformed and were clearly visualized  A fn the LG . By trachigh eaterlow before sigmoid differentiation or domehigh eaterhigh before linear differentiation) prior to the beginning of changes in dome and/or eater levels following differentiation. The time point where we can record such changes was therefore the exit point from the steady state and was denoted as ‘frame 0 or 0 hr’. To quantify differentiation, the videos were saved as RGB stacks for the Fiji TrackMate plugin )/ - min ); Norm.R = )/ - min )) where the difference between the fluorescent values at every time point  and the minimum fluorescent value was divided by the difference between the maximum fluorescent value and the minimum fluorescent value. Minimum and maximum fluorescent values were obtained across the two markers, as shown by the ‘min’ and ‘max’ functions of the equation. By using this approach, the patterns, trends, and relative differences between two markers were all preserved to make comparisons across the videos. Moreover, we confirmed that the normalization method preserved the trends of the markers during differentiation  Imaging: Entire LGs of genotype dome-MESO-GFP; eater-dsRed were imaged by a FV1000 microscopy with a step size 1.5 μm using exactly equivalent laser settings across wild-type control and infection groups. Importantly, the TrackMate-based automatic method used to perform histo-cytometry analysis works best on single sections with cells clearly separated from each other. To unbiasedly select slices across samples and different groups, we took the slides that are located at 25%, 50%, and 75% of the total thickness (or z axis) of the LG. The imaged LG slides were saved as OIB files in Fluoview and inputted into Fiji as RGB stacks (in.tiff) for the following analysis. (2) Segmentation: To segment individual nuclei in the imaged LGs, the automatic ROI selection tool implanted in the TrackMate plugin with a LoG detector was used. The Blob diameter was set as 13 and the threshold was set as 2.5 in TrackMate across all LG images to reliably select all nuclei. Using the method, in total 2946 cells  from the wild type LGs and 2985 cells from the LGs following infection  were captured. From these cells, an Excel-based method (using the rand function) was performed to completely randomize their order and then took 2500 cells randomly from the two groups that were used for the To perform in vivo analysis on LGs, a method of histo-cytometry  was appl2) and plotted in GraphPad Prism.A gstD-GFP reporter line  was usedE. coli  was used in this study. E. coli was grown in the LB medium  overnight in 37℃ for infection experiments. A larval oral infection protocol was applied with slight custom modifications  for 6 hr in 25℃. Larvae at 78 AEL were infected for 6 hr and dissected at 84 AEL for ex vivo live imaging. The infected larvae were first screened under a fluorescent stereo microscopy  to confirm that they ingested a large amount of GFP expressing E. coli . The larvae were then used for long-term imaging experiments or in vivo analysis.Ampicillin-resistant, GFP expressing ications . Larvae Statistics were performed using GraphPad Prism (ver. 6). p Values were determined using statistical tests that were detailed in all figure legends and Source data files. The sample size of each analysis was indicated in figure legends. No statistical method was performed to pre-determine sample size. Drosophila larval hematopoietic organ called the lymph gland. The new method allows tracking the proliferation and differentiation of progenitor cells ex vivo and provides insights into the modes of differentiation during development and infection. The evidence supporting this is convincing but would be further strengthened if the authors explain the disparity between the speed and proportion of two modes of differentiation as the reviewers suggested.This study represents an important technical advancement in the live-imaging and analytical approaches to the  public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Utpal Banerjee as the Senior Editor. The reviewers have opted to remain anonymous.Thank you for submitting your article "Kinetics of blood cell differentiation during hematopoiesis revealed by quantitative long-term live imaging" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:1. Substantiate statistical analysis.Since this study is based on the analysis of live imaging, detailed data analysis, interpretation, and presentation are all core to the paper. All the reviewers raised questions regarding data analysis/presentation or statistics. Please find Rev#1 points 5-6, Rev#2 points 1,2,4,5,6, and Rev#3 points 3,6.2. Provide additional information about the lymph gland development following ex vivo culture.Rev#1 and 3 raised questions about how relevant the lymph gland development/response is compared to in vivo status after ex vivo culture. How similar the lymph gland is after ex vivo culture in terms of their growth? How faithful ex vivo culture is in mimicking in vivo conditions after infection? Details can be found in Rev#1 points 2-3 and Rev#3 points 1-2.3. Validate the two modes of differentiation.Rev#1 and Rev#2 raised questions as to whether the two modes both give rise to the true mature blood cell. Additional spatial and temporal analysis on progenitors or mature blood cells originating from two modes needs to be performed. For example, do both modes lead to the differentiation of P1+ true mature blood cells during development or upon infection? Please find details from Rev#1 point 4.4. Add essential references. Please find Rev#3 point 4.Reviewer #1 (Recommendations for the authors):eLife.Despite the significant advancement made in this study, there are several points to be clarified before publication in 1. It could be a trivial issue, but it was hard for the reviewer to follow the main and supplementary figures along the text as figures are not arranged linearly and some figure callings are even missing in the text.2. For the infection experiment; although the authors showed that oral infection induces the differentiation of P1+ plasmatocytes or Hnt+ crystal cells , it is unclear at which time point they dissected the lymph gland following infection and how much the lymph gland was differentiated at the point of observation . Lymph glands in vivo would receive systemic immune factors over the immune activation while lymph glands taken out would be devoid of such signals. Thus, differentiation phenotypes of the lymph gland could differ depending on how long the larvae were exposed to a systemic in vivo environment versus an ex vivo culture system. For example, lymph glands being dissected after 6 hours post-infection may show a different differentiation program when compared to lymph glands dissected after 12 or 24 hours post-infection.3. Concerning the above point, how is the ex vivo culture system biologically relevant following infection, and how close is it compared to lymph glands in vivo? Do lymph glands cultured ex vivo activate immune-responsive genes or representative immune markers comparable to lymph glands in vivo following infection?4. The authors proposed two different modes of differentiation, sigmoidal and linear. This is a compelling finding and provides interesting insights; however, it is unclear when and where the two modes of differentiation occur during lymph gland development. Given that the sigmoidal mode initiates with Dome_high & eater_low progenitors, the sigmoid mode of differentiation seems to appear in the typical medullary zone. Dome_high abruptly drops to Dome_low and eater_low rises at the end but the level of eater is still low compared to the starting point of the linear mode. However, the linear mode starts with Dome_high & eater_high population, which could indicate the intermediate progenitors. In fact, the authors showed that the linear mode is abundant in the prospective intermediate region  while the ratio of sigmoid mode is higher near the heart . Therefore, the two different modes of differentiation may not go parallel but are consecutive. The authors describe the two modes as if both modes take place to finally differentiate progenitors into mature hemocytes, but this may not be the case. Temporal ambiguity of the two modes hinders the significance of this finding and the authors need to clarify whether the two modes occur in parallel within the same region of the lymph gland.5. The authors showed that the sigmoid type differentiation induces the eater and turns off the dome . But absolute GFP/RFP intensities changed in this process are rather marginal in Fig7A compared to Figure 5C-D or Fig7A'. A similar discrepancy is seen in Fig7C-D and FigC'-D'. The infection seems to cause a significant increase in eater-RFP in Fig7D' but the absolute value change in Fig7D shows eater rises from 25 to 35ish. Thus, it is not clear how the "norm intensity" reliably represents the absolute value of GFP and RFP in this presentation. How do cells used in the analyses look? Which graph matches better with the real expressions? And do they divide?6. Statistics are missing in some graphs. In Fig7N, each data points need to be presented rather than showing dynamite plots. Fig1F represents a small sample size and would be better showing each data point rather than a mean value.Reviewer #2 (Recommendations for the authors):1. The explanation in line 431 seems incorrect, as the increased number of high high cells upon infection is equivalent to the decreased number of low low cells, and thus would not lead to an increase in the absolute number of differentiated cells. upon infectionSee my hypothesis in the public review; perhaps the analysis has missed that cells remain in the intermediate state in the sigmoidal path for a very long time.2. Figure 5C-D. Not clear to me what the scatter represents.Are these multiple different cells from the same lymph gland tracked over time?3. Figure 6C-F, could the authors show some sort of actual graph of the LG to indicate where these changes in the division rate are?This isn't obvious to the non-initiate if this is at the edge, or where.4. Figure 7A', "-D', " should be plotted with the same x-axis time course to be able to compare the dynamics from one to the other.5. Need higher n in Figure 7F.6. Please show how many cells are in the P category for both conditions in Figure 7N.Reviewer #3 (Recommendations for the authors):To strengthen the science:1. The authors should compare ex vivo GstD-GFP and Sytox levels to those seen in fixed samples (in vivo), similar to what was done with EdU and cell cycle in Figure S3. It is important to know whether this ex vivo system maintains similar ROS and cell death levels as that seen in vivo to help determine the limitations of the ex vivo co-culture system.a. For example, based on past studies of ROS in the LG , one would expect to see higher GstD-GFP levels in the progenitors/MZ than in differentiated cells/CZ. But in Figure S2C, that doesn't seem to be the case. ROS levels seem to be low throughout the LG. Please explain.2. The authors should also compare the numbers/ratios of progenitors and differentiated cells at the beginning and end of live imaging with in vivo LGs from age-matched larvae. This would help determine whether the process of differentiation they follow ex vivo produces similar results as that seen in vivo.a. While this is beyond the scope of the current paper, staining or imaging of key molecules that are expected to be high in progenitors  or differentiated cells  and comparing levels in vivo vs ex vivo would be helpful validation of the system.3. Correlation coefficient analysis is inconsistent. In the methods, the authors say 0 to 0.5 are defined as a "weak correlation". 0 to 0.5 is a wide range of correlation and is not consistent with standard statistical practices that categorize 0 to 0.25 as not correlated, 0.25 to 0.5 as weak correlation, 0.5 to 0.75 as moderate correlation, and 0.75 to 1 as strong correlation.a. In fact, on lines 385-395, a correlation coefficient of 0.14 is interpreted as "not greatly altered", while 0.45 is interpreted as "marked differences", and 0.49 as "a general increase".Considering a correlation coefficient below 0.25 as not correlated is more in line with standard practices but not consistent with the authors' explanation in the methods.i. The authors should update their methods to reflect a more standard, and consistent, interpretation of correlation coefficients. If there is reason to believe this standard does not apply here, please explain.b. Furthermore, in lines 311-314, the authors say that a correlation coefficient of 0.44 is consistent with a "weak correlation". There is no substantial difference between 0.44, 0.45, and 0.49, therefore either all should be considered "weak correlations" or all are correlated, the authors should not use or imply different terms for these very similar values. Please update the language to reflect a more consistent interpretation of correlation coefficients.4. In line 463, the authors say that Hml (but not Pxn) is a mature blood cell marker, and yet both this paper , as well as Spratford et al. 2021 and Girard et al. 2021, shows Hml is an intermediate progenitor marker and therefore not a mature cell marker. It is curious that these papers are not mentioned when mentioning other papers on intermediate progenitors (e.g. lines 87-90 and lines 458-460) as they present compelling evidence that Hml/dome-meso marks intermediate progenitors  and that IPs serve as a transitional state between progenitors and differentiated cells.5. One caveat not noted in the manuscript is that because the authors use Eater-Dsred to monitor differentiation they are only able to follow differentiation events leading to plasmatocytes and not crystal cells .6. Some statistics seem to be missing and it is not clear if statistical tests were not performed or if they were performed but found to be not significant. Several conclusions and claims that the authors state  seem to be based on data without statistics and so should either be interpreted with that in mind or it should be explained why the stats could not be performed if this is true. Examples include:a. Figure 6B, is there any significant difference between linear diff in WT vs infection or sigmoid diff in WT vs infection?b. Figure 7E, are there any significant differences between WT and infection in those graphs?i. For example, whether these changes are significantly different affect the authors' conclusions in lines 413-416c. Figure 7N, are there any significant differences between WT and infection in those graphs? (be careful here how sample size (n=) is defined because each individual lobe can be considered a replicate but not each cell.)d. Figure 7I-N, why are there so few cells? Were the number of cells in each lobe counted in 3D? If so, there should be many more cells total for 8 lobes. Please explain.e. Actual p-values should be included in the figure legends (stars can be kept in the figures themselves). Reviewer #1 (Recommendations for the authors):Despite the significant advancement made in this study, there are several points to be clarified before publication in eLife.1. It could be a trivial issue, but it was hard for the reviewer to follow the main and supplementary figures along the text as figures are not arranged linearly and some figure callings are even missing in the text.Thank you for pointing this out. We have gone through the manuscript systematically to ensure a linear order of figure callouts and make sure all figure callouts are where they are supposed to be.2. For the infection experiment; although the authors showed that oral infection induces the differentiation of P1+ plasmatocytes or Hnt+ crystal cells , it is unclear at which time point they dissected the lymph gland following infection and how much the lymph gland was differentiated at the point of observation . Lymph glands in vivo would receive systemic immune factors over the immune activation while lymph glands taken out would be devoid of such signals. Thus, differentiation phenotypes of the lymph gland could differ depending on how long the larvae were exposed to a systemic in vivo environment versus an ex vivo culture system. For example, lymph glands being dissected after 6 hours post-infection may show a different differentiation program when compared to lymph glands dissected after 12 or 24 hours post-infection., conditions and in vivo conditions. However, we think our data does address this issue at least partially as outlined below.The reviewer raises two excellent points, ones we have thought about a lot and took into consideration. First, we would like to state that reading the reviewer’s comments here, as well as other reviewer comments, convinced us we needed to add a paragraph to the discussion spelling out the possible limitations of the ex vivo culture method. This new paragraph is the second paragraph of the Discussion line 493-503 in the revised manuscript and directly brings up the reviewer concerns expressed above. Specifically, it is difficult to prove beyond doubt that differentiation is identical following infection in cultured, ex vivoa) The reviewer asks about the persistence of systemic signals after dissection and start of the ex vivo culture procedure following infection versus in vivo LGs. In particular the reviewer is concerned that lymph glands at 6 hrs post-infection may show a different differentiation program compared to lymph glands at 12 or 24 hours post-infection. We agree this is a legitimate concern, that points to one of the limitations of the kind of ex vivo culture protocols we use here. We attempted to address this issue by using the infection protocol on the same population of larva and then splitting them into a group that was analyzed in vivo , and a group that was analyzed with the ex vivo culture protocol. The results are presented in Figure 6 —figure supplement 1. The in vivo data is somewhat more variable but we do not detect statistically significant differences between the two groups. This data is shown now discussed in the Results line 405-407.b) The paper includes several other comparisons of the behavior of ex vivo and in vivo models following infection. As far as we could see, within the limit of looking at dynamic data ex vivo versus static data for the in vivo, the two groups behaved quite similarly. For example, when we looked at proliferation , the proportion of blood progenitors undergoing mitosis decreased in a similar manner both in vivo (assayed using pH3) and ex vivo LGs (assayed using live imaging). Another example, is looking at differentiation, between the two groups we see similar trends in terms of number of differentiation events , and type of differentiation events . We now created a new section in Discussion line 493-500 to highlight potential technical limitations such as this and our efforts to mitigate them.c) To answer the reviewer’s question we started sustained oral infection 78 hours after egg laying, and dissected at 84 hours after egg laying. This is a shorter infection protocol than the one we used previously  where infection lasted for 12 hours. This information is outlined in the Methods section line 1096-1097.3. Concerning the above point, how is the ex vivo culture system biologically relevant following infection, and how close is it compared to lymph glands in vivo? Do lymph glands cultured ex vivo activate immune-responsive genes or representative immune markers comparable to lymph glands in vivo following infection?eater encodes the protein that is essential for plasmatocytes to phagocytose bacteria . Eater itself has also been used previously as a readout of immune responsive gene or an immune marker . It has been shown that the expression of eater gene is transcriptionally regulated  and that following infection, the expression of eater was upregulated . If the reviewer looks at Figure 7J (not infected) versus Figure 7L (infected) they will notice a general shift of the blood cells to higher levels of eater-dsRed expression in essentially all the cell types. This data is by no means conclusive but is suggestive of immune activation. We now mention this in the Results section line 459-461.As we highlight in the reply to point 2 above we observe similarities in the in vivo and ex vivo response following infection. The additional question asked by the reviewer here, whether we see an elevation of immune markers in the ex vivo culture is an intriguing one. We believe some of our data already provides answers. Specifically, we employed the eater-dsRed enhancer trap line to visualize plasmatocytes. The gene 4. The authors proposed two different modes of differentiation, sigmoidal and linear. This is a compelling finding and provides interesting insights; however, it is unclear when and where the two modes of differentiation occur during lymph gland development. Given that the sigmoidal mode initiates with Dome_high & eater_low progenitors, the sigmoid mode of differentiation seems to appear in the typical medullary zone. Dome_high abruptly drops to Dome_low and eater_low rises at the end but the level of eater is still low compared to the starting point of the linear mode. However, the linear mode starts with Dome_high & eater_high population, which could indicate the intermediate progenitors. In fact, the authors showed that the linear mode is abundant in the prospective intermediate region  while the ratio of sigmoid mode is higher near the heart .Developmental Biology and Spratford et al., 2021 Development papers, are defined as expressing both the mature hemocyte marker Hml and the progenitor marker Dome. In our study, we used Dome and eater, not Dome and Hml. We used eater and not Hml is because Hml also marks crystal cells . It is possible we were cautious to a fault here so we have now added in the Discussion line 527-529 to mention that cells undergoing linear type differentiation are likely to be intermediate progenitors.Our thinking is very much in line with the reviewer’s suggestion that the cell population we identify as undergoing the linear trajectory overlaps with the population previously identified as “intermediate progenitors''. This is based on the location where they differentiate and the profile of markers they express. However we did not state this outright out of abundance of caution. The population of “intermediate progenitors” as described in Blanco-Obregon et al., 2020 Therefore, the two different modes of differentiation may not go parallel but are consecutive. The authors describe the two modes as if both modes take place to finally differentiate progenitors into mature hemocytes, but this may not be the case. Temporal ambiguity of the two modes hinders the significance of this finding and the authors need to clarify whether the two modes occur in parallel within the same region of the lymph gland.We should have mentioned this but we never observed the consecutive mode described by the reviewer in hundreds of hours imaging lymph glands. That is, we did not observe a linear type followed by a sigmoid differentiation or a sigmoid type followed by a linear differentiation. As the reviewer notes “the level of eater is still low compared to the starting point of the linear mode”, therefore our data is not consistent with a consecutive model. Nonetheless, in an attempt to address the “temporal ambiguity” pointed out by the reviewer we decided to specifically show that the two pathways could either occur in parallel or at different time points within the same region of the lymph gland. This analysis of differentiation trajectories in both space and time  is shown in Figure 6. We searched in our data for areas where we could find cells undergoing both the sigmoid and linear types of differentiation occurring in the same area in lymph glands from control and infected larva. Shown in Figures 6D and 6F (wildtype control) and Figure 6E and 6G (infected larva) are regions where we identified cells undergoing the two types of differentiation trajectories in the same region. We then followed each of these cells individually to determine when the differentiation event took place. We identified the time point where differentiation initiated and represented it as the percentage of time into the movie . For example, in the wild type, we show two cells (cell 1 and cell 2) in box#1 that underwent sigmoid and linear type differentiation, respectively, starting at the same time while the cells in the box labeled #2 underwent differentiation that started at different time points. In infection group for example, we saw in box#1 Cell 2 and Cell 3 that underwent sigmoid and linear differentiation, respectively, starting at the same time point while other cells in other boxes all differentiate at different time points. This data shows that parallel differentiation takes place, it of course does not discount the “consecutive model” but as we mention above, we have not directly observed such a trajectory. So in conclusion, to date we clearly see the two modes of differentiation occurring in parallel or at different time points but never observed them occurring consecutively. This new data is shown in Figure 5—figure supplement 1 and discussed in line 394-400 and 420-423 of the Results section.Drosophila, Nat Coms, 2020). Their data suggests that there is a PH4-PM1-PM2 path (which contains an intermediate state during differentiation) and a PH5/6-PM3/4 path (which contains no intermediate state). PH: progenitors; PM: plasmatocytes.We now also mention in the Discussion line 508-512 that our data is in line with single cell sequencing data for the LG that has shown that there are multiple distinct paths that a progenitor can differentiate into a plasmatocyte . But absolute GFP/RFP intensities changed in this process are rather marginal in Fig7A compared to Figure 5C-D or Fig7A'. A similar discrepancy is seen in Fig7C-D and FigC'-D'. The infection seems to cause a significant increase in eater-RFP in Fig7D' but the absolute value change in Fig7D shows eater rises from 25 to 35ish. Thus, it is not clear how the "norm intensity" reliably represents the absolute value of GFP and RFP in this presentation. How do cells used in the analyses look? Which graph matches better with the real expressions? And do they divide?We thank the reviewer for pointing this out as we should have explained better the data shown in Figure 7 and Figure 5, which would have prevented this misunderstanding. First we wanted to explain why and how we normalized the data shown in Figures 5C-D and in Figure 7C-D. The reason expression needs to be normalized is in order to be able to compare signals across multiple samples and several independent experiments. Our normalization protocol is a modified version of the one established by Martin et al., . The original normalization method from Martin et al., 2018 required modifications since it was designed for a situation where one marker gradually changes over time while the other marker does not change over time. In comparison, in our present study on LGs we dealt with a scenario where both markers used (dome-MESO-GFP and eater-dsRed) gradually changed over time.We have taken the reviewer’s concern to heart and went back to our dataset and re-analysed it to compare normalized and raw data. The results of this analysis are presented in Figure 7—figure supplement 2 and in Methods line 1041-1043, raw data shown in the two bars on the left and normalized data on the two bars on the right. We think this data argues that normalization does not change the overall interpretation.We have included with the manuscript movies (Video 6-9) corresponding to Figure 7A-7D that illustrate how the cells look and how intensities of dome GFP and eater-dsRed change overtime. The colors used in the Videos S6-S9 was based on raw intensities of dome GFP and eater-dsRed. We now also included the images in Figure 7 —figure supplement 1 showing how a single cell from Figure 7A-7D looked like.Finally, we did not observe cells undergoing differentiation and mitosis at the same time nor cells divide after they differentiate in all our live imaging videos.6. Statistics are missing in some graphs. In Fig7N, each data points need to be presented rather than showing dynamite plots. Fig1F represents a small sample size and would be better showing each data point rather than a mean value.We again apologize for not being clearer about this but the data shown in figure 7N is simply an unprocessed raw number, not an average, from a total cell count of cells from 6 primary lobes of 3 LGs (now mentioned in Methods line 1055-1076). In other words, what is shown is the complete data set for 3 entire lymph glands, specifically the control lymph glands shown in Figure 7J and the lymph glands from infected larvae shown in Figure 7L. This was indicated in Results (line 471) and figure legend (line 780-781) sections. That is, Figure 7N showed the total number of cells  in each quadrant of 7J and 7L. The benefit of this  is that showing the numbers of cells in each quadrant helps the reader interpret the data more easily. We made this explicit in the figure legends now.Also, as the reviewer suggested we modified Figure 1F to show all data points.Reviewer #2 (Recommendations for the authors):1. The explanation in line 431 seems incorrect, as the increased number of high high cells upon infection is equivalent to the decreased number of low low cells, and thus would not lead to an increase in the absolute number of differentiated cells. upon infectionSee my hypothesis in the public review; perhaps the analysis has missed that cells remain in the intermediate state in the sigmoidal path for a very long time.We agree with the reviewer that we probably overstated the link between the change in population of the two intermediate populations (dome low eater low and dome high eater high). We have rewrote this section (now in line 467-477) to be descriptive and clearly state the parts that are speculative.2. Figure 5C-D. Not clear to me what the scatter represents.Are these multiple different cells from the same lymph gland tracked over time?Sorry for not making this clearer but both Figure 5C and Figure 5D represent a single cell that was tracked over time. In Figure 5C, each dot represents the fluorescent intensity of dome-GFP or eater-dsRed at that time point, while 5D shows the ratio of the green and red signal at that time point. We modified the figure legends of Figure 5C-5D in line 733-734 and line 736-737 to state this in clearer fashion.3. Figure 6C-F, could the authors show some sort of actual graph of the LG to indicate where these changes in the division rate are?This isn't obvious to the non-initiate if this is at the edge, or where.To make this clearer to our readers we previously included the following image with the grid we used overlaying on actual lymph gland . Left panel from Figure 4 —figure supplement 2 and right panel from Figure 6C show the relationship between a heatmap and an actual LG. The coordinates of the two graphs are in the same orientation (see the pink dot as a reference point).4. Figure 7A', "-D', " should be plotted with the same x-axis time course to be able to compare the dynamics from one to the other.This is a fair request but a bit of a tricky one because the differences in time between the linear and sigmoid trajectory meant that the data looked compressed or elongated when plotted at identical scales in different trajectories making it hard to interpret the data. However, in line with the reviewer we now made the x axis of Figure 7A’-A’’ and 7B’-7B’’ uniform and the x axis in Figure 7C’-7C’’ and 7D’-7D’’ uniform. This makes comparison within trajectories easier to compare yet still allows the shape of the curve to be apparent.5. Need higher n in Figure 7F.We want to assure the reviewer that we tried! When compared to a lymph gland from an infected, or immune-activated, larva, wildtype controls do not exhibit a great deal of differentiation at this developmental stage . It took a great deal of effort to get the sample size we presented in the previous version of Figure 7F. Nonetheless, we did our best to increase the sample size for WT linear differentiation and the figure is now in Figure 7G.6. Please show how many cells are in the P category for both conditions in Figure 7N.We have added this information into Figure 7N.Reviewer #3 (Recommendations for the authors):To strengthen the science:1. The authors should compare ex vivo GstD-GFP and Sytox levels to those seen in fixed samples (in vivo), similar to what was done with EdU and cell cycle in Figure S3. It is important to know whether this ex vivo system maintains similar ROS and cell death levels as that seen in vivo to help determine the limitations of the ex vivo co-culture system.We have added the GstD-GFP experiment the reviewer asked for, and added it now in Figure 1 —figure supplement 2C. Our results show that our ex vivo system maintains similar ROS levels as that seen in vivo. We now mention this data in Results line 160-162.rd instar lymph glands ; Figure 1 of Chiu H. and Govind S., 2002, Cell death & differentiation (doi: 10.1038/sj.cdd.4401134); Figure 3G of Yu S et al., 2021, eLife (doi.org/10.7554/eLife.60870); Figure S7A from Khadilkar et al., 2020, Cur Biol (doi.org/10.1016/j.cub.2020.06.027); and Figure 6A, 6B, 6C, 6H from Araki et al., 2019, Diseases Models and Mechanisms (doi.org/10.1242/dmm.037721). We therefore conclude that the number of dying cells in the ex vivo cultured LGs falls into the normal range that is expected to be observed in in vivo LGs with 12 hours of culture. We now mention this explicitly in the text in Results line 168-172. However, after about 12.5 hours the rate of cell death under the ex vivo culture conditions we begin to see a rate of cell death that is higher than that reported in the controls. Indicating that beyond 13 hours the ex vivo culture is less reliable. This is why we capped all out movies well before we got to this time point.As for analysis of cell death, we are lucky to have access to many studies that measured the numbers of dying cells in lymph glands in vivo in 3rd instar larvae which we can compare to our ex vivo culture conditions. In ex vivo culture, we showed that following 12.5 hrs of culture period, the average number of dying cells ranged from 2-10 cells , one would expect to see higher GstD-GFP levels in the progenitors/MZ than in differentiated cells/CZ. But in Figure S2C, that doesn't seem to be the case. ROS levels seem to be low throughout the LG. Please explain.The images in the previous Figure S2C  indeed do not display a very obvious difference in the level of gstD-GFP between MZ and CZ. However, it is hard to tell since we did not include the red channel (to image eater-dsRed) that can help to distinguish CZ from MZ or a DIC channel that can help to visualize an entire LG. In images with the DIC channel included, the difference between MZ and CZ see  is quite2. The authors should also compare the numbers/ratios of progenitors and differentiated cells at the beginning and end of live imaging with in vivo LGs from age-matched larvae. This would help determine whether the process of differentiation they follow ex vivo produces similar results as that seen in vivo.As suggested by the reviewer we now included data comparing the ratios of differentiated cells to progenitors at various time points from ex vivo wild-type LGs and in vivo wild-type LGs from age-matched larvae . Specifically, although slight variation was observed at different time points, which is to be expected in an evolving system such as the lymph gland, differences in the ratio observed between the ex vivo and in vivo conditions were not statistically significant. See line 345-348.a. While this is beyond the scope of the current paper, staining or imaging of key molecules that are expected to be high in progenitors  or differentiated cells  and comparing levels in vivo vs ex vivo would be helpful validation of the system.The stainings suggested by the reviewer are all excellent suggestions and we will add these to our to-do list for the future.3. Correlation coefficient analysis is inconsistent. In the methods, the authors say 0 to 0.5 are defined as a "weak correlation". 0 to 0.5 is a wide range of correlation and is not consistent with standard statistical practices that categorize 0 to 0.25 as not correlated, 0.25 to 0.5 as weak correlation, 0.5 to 0.75 as moderate correlation, and 0.75 to 1 as strong correlation.a. In fact, on lines 385-395, a correlation coefficient of 0.14 is interpreted as "not greatly altered", while 0.45 is interpreted as "marked differences", and 0.49 as "a general increase".Considering a correlation coefficient below 0.25 as not correlated is more in line with standard practices but not consistent with the authors' explanation in the methods.i. The authors should update their methods to reflect a more standard, and consistent, interpretation of correlation coefficients. If there is reason to believe this standard does not apply here, please explain.b. Furthermore, in lines 311-314, the authors say that a correlation coefficient of 0.44 is consistent with a "weak correlation". There is no substantial difference between 0.44, 0.45, and 0.49, therefore either all should be considered "weak correlations" or all are correlated, the authors should not use or imply different terms for these very similar values. Please update the language to reflect a more consistent interpretation of correlation coefficients.This is a very good point. We needed to have a consistent standard for correlation and we now use the following suggested standard: 0 to 0.25 as not correlated, 0.25 to 0.5 as weak correlation, 0.5 to 0.75 as moderate correlation, and 0.75 to 1 as strong correlation. We modified the text in the Results accordingly as well as explained in the Methods section line 927-931.4. In line 463, the authors say that Hml (but not Pxn) is a mature blood cell marker, and yet both this paper , as well as Spratford et al. 2021 and Girard et al. 2021, shows Hml is an intermediate progenitor marker and therefore not a mature cell marker. It is curious that these papers are not mentioned when mentioning other papers on intermediate progenitors (e.g. lines 87-90 and lines 458-460) as they present compelling evidence that Hml/dome-meso marks intermediate progenitors  and that IPs serve as a transitional state between progenitors and differentiated cells.Thanks for pointing this out, we edited the text in line with the points raised and added the missing citations into line 87-88 and line 512-514 (the previous 458-460).5. One caveat not noted in the manuscript is that because the authors use Eater-Dsred to monitor differentiation they are only able to follow differentiation events leading to plasmatocytes and not crystal cells .Using our long term live imaging protocol to capture crystal cell differentiation will definitely be an important goal of our future project. The reason why we did not study crystal cells currently is because we did not have a line to label crystal cells in the red channel (the BcF6GFP enhancer trap line which would work is GFP based) which we need to be used together with dome-MESO-GFP. We now inserted a mention of this caveat in Discussion line 500-503.6. Some statistics seem to be missing and it is not clear if statistical tests were not performed or if they were performed but found to be not significant. Several conclusions and claims that the authors state  seem to be based on data without statistics and so should either be interpreted with that in mind or it should be explained why the stats could not be performed if this is true. Examples include:a. Figure 6B, is there any significant difference between linear diff in WT vs infection or sigmoid diff in WT vs infection?We now performed the statistical tests requested in Figure 6B. There is a statistically significant difference in the linear differentiation trajectory between wild type LGs and LGs from infected larvae while there is no statistically significant difference in the sigmoid differentiation trajectory between wild type LGs and LGs from infected larvae. We updated Figure 6B to show this. Importantly, this does not impact our interpretation of the data in the Results.b. Figure 7E, are there any significant differences between WT and infection in those graphs?i. For example, whether these changes are significantly different affect the authors' conclusions in lines 413-416We now performed the requested statistical analysis on the previous Figure 7F-7H . The conclusion now reads “Taken together, the data is consistent with a model whereby infection causes higher differentiation in the LGs not by increasing the rate of differentiation but rather by inducing a shift from one type of differentiation, the sigmoid trajectory, to another, the linear trajectory.” and is in line with the result of the statistical test (now described in line 445-448).c. Figure 7N, are there any significant differences between WT and infection in those graphs? (be careful here how sample size (n=) is defined because each individual lobe can be considered a replicate but not each cell.)Figure 7N was designed to show the total number of cells in each quadrant of Figure 7J and 7L. Each bar represents a single value which shows the sum of the number of cells in each quadrant. We now explicitly stated the number of cells as well in each category of wild type and infection in the text where we describe the Results of Figure 7N in line 467-471.d. Figure 7I-N, why are there so few cells? Were the number of cells in each lobe counted in 3D? If so, there should be many more cells total for 8 lobes. Please explain.We have to apologize, this is definitely our mistake here, and we thank the reviewer by pointing this out.First, we made a mistake in the sample size that was listed in the previous version of the graph (however the source data file had the correct information). Specifically, the sample size is 3 LGs that contain 6 primary lobes. We now update sample size on the graph of Figure 7I-N, figure legends (line 774-776), and Results (line 454-455).Second, the TrackMate-based automatic method we used to perform histo-cytometry analysis can only work on single sections (not a stack) with cells clearly separated from each other. To unbiasedly select slices across samples and different groups, we took the slides that are located at 25%, 50%, and 75% of the total thickness (or z axis) of the LG. That means from each LG, we sampled three slides that are fed into the Fiji TrackMate plugin. Using this method, we took in total 2946 cells  from the wt LGs and 2985 cells from the LGs of infection . From these cells, we first performed an Excel-based method (using the rand function) to completely randomize their order and then took 2500 cells randomly from the two groups that were used for the Figure 7I-N. We now corrected the sample size on the figure and include the above explanation into the Methods section line 1055-1076.e. Actual p-values should be included in the figure legends (stars can be kept in the figures themselves).Although all P values were already reported in the Source data files of each figure that we included, we followed the reviewer’s suggestion to put them in the actual figure legends of every figure to make them easier to find.


To identify the impact of enrolment onto a national pulse oximetry remote monitoring programme for COVID-19  on health service use and mortality in patients attending Emergency Departments (EDs).We conducted a retrospective matched cohort study of patients enrolled onto the CO@h pathway from EDs in England. We included all patients with a positive COVID-19 test from 1 October 2020 to 3 May 2021 who attended ED from 3 days before to 10 days after the date of the test. All patients who were admitted or died on the same or following day to the first ED attendance within the time window were excluded. In the primary analysis, participants enrolled onto CO@h were matched using demographic and clinical criteria to participants who were not enrolled. Five outcome measures were examined within 28 days of first ED attendance: (1) Death from any cause; (2) Any subsequent ED attendance; (3) Any emergency hospital admission; (4) Critical care admission; and (5) Length of stay.15 621 participants were included in the primary analysis, of whom 639 were enrolled onto CO@h and 14 982 were controls. Odds of death were 52% lower in those enrolled (95% CI 7% to 75%) compared with those not enrolled onto CO@h. Odds of any ED attendance or admission were 37% (95% CI 16% to 63%) and 59% (95% CI 32% to 91%) higher, respectively, in those enrolled. Of those admitted, those enrolled had 53% (95% CI 7% to 76%) lower odds of critical care admission. There was no significant impact on length of stay.These findings indicate that for patients assessed in ED, pulse oximetry remote monitoring may be a clinically effective and safe model for early detection of hypoxia and escalation. However, possible selection biases might limit the generalisability to other populations. Hypoxia is known to be an important predictor of mortality and the need for hospital admission in patients with COVID-19.The NHS COVID-19 Oximetry @home (CO@h) programme provided pulse oximeters to people with COVID-19 in the community in England, to support self-monitoring and early detection of hypoxia but the clinical effectiveness was unknown.This study found that in patients assessed in EDs and who were not admitted within 24 hours, those enrolled in the programme had significantly lower mortality and requirement for critical care within 28 days than those not enrolled.Patients enrolled to the programme had higher odds of subsequent ED attendance and emergency hospital admission suggesting early recognition of hypoxia and escalation of care.Our findings suggest the CO@h programme is a safe pathway for patients with COVID-19, and with some evidence of a benefit on mortality, but potential selection bias in patients enrolled on the programme may limit the generalisability to other populations.The COVID-19 pandemic has placed a huge demand on health systems around the world and led to an increase in use of digital technologies in public health responses and healthcare settings.Early in the pandemic, it was recognised that hypoxia is a key prognostic marker and is strongly associated with mortality from COVID-19.Initial eligibility criteria for CO@h included adults aged 65 years or over, those designated as clinically extremely vulnerable (CEV), or where clinical judgement applied, although eligibility could vary across sites.Current evidence for the effectiveness of the programme is lacking, with two previous analyses as part of the evaluation of the CO@h programme showing no impact on mortality at a population level.This study used a retrospective matched cohort design. The eligible cohort included all people resident in England with a positive COVID-19 test result between 1 October 2020 and 3 May 2021, who attended an NHS ED in England within a 14-day time window from 3 days before to 10 days after the date of their positive test. For all eligible patients, an ED index date was created as the first ED attendance date within this time window. For those enrolled, the attendance date on the same day or day prior to enrolment, within the time window, was used. Patients who were admitted to hospital or died on the same or following day to their index ED attendance  were excluded. Patients admitted to hospital in the 14 days before their index ED attendance were also excluded, as were care home residents, as different monitoring pathways may have operated for these groups.Death from any cause.One or more ED attendances.One or more emergency hospital admissions.One or more critical care admissions .Total hospital length of stay in days, of those admitted who did not die within 28 days.Data on patients enrolled to the CO@h programme were submitted from participating sites via NHS Digital’s Strategic Data Collection Service.Demographic data, including age, sex, ethnicity and lower layer super output area (LSOA) of residence were derived from GDPPR, or, if missing, from HES or ECDS. Deciles of the Index of Multiple Deprivation (IMD) 2019 were linked to LSOA of residence.10.1136/emermed-2022-212377.supp1Supplementary dataUnivariable logistic regression was used to estimate the odds ratio (OR) for each of the four binary outcomes in those enrolled compared with controls and negative binomial regression was used to estimate the treatment effect of the programme on length of stay. Analyses of length of stay excluded patients who died during admission within the 28-day time window.a priori. Patients with missing values for any of the matching covariates were excluded from analysis. The characteristics of patients who could not be matched by the algorithm (because no control was available) were described and differences compared using χ2 tests. After matching, regression models were run including stratum-specific weights from the matching algorithm to account for unequal stratum sizes. Enrolled patients who were unmatched were excluded from the matched analysis. A subgroup analysis was performed for patients aged 50 years or more, using the matched model.In the primary analysis, to account for potential differences in patient characteristics between groups, those enrolled to CO@h were matched to those not enrolled based on the following variables: age category, sex, ethnicity, terciles of IMD score, BMI category, month of ED index date, CEV status and days from COVID-19 test to ED index date . The variables for inclusion in the model were chosen A series of sensitivity analyses were carried out for the primary analysis to assess the sensitivity of inferences to changes in the model assumptions. The first compared the impact of changing the exclusion timeframe between ED attendance and admission/death from 1 day to (1) The same day only or (2) Within 2 days. The second sensitivity analysis assessed potential differences in COVID-19 outcomes between the enrolled versus control groups, for two outcomes:Twenty-eight days mortality using only deaths where COVID-19 was listed as the primary cause of death.Twenty-eight days emergency hospital admissions where COVID-19 was listed as a primary or secondary diagnosis.Two additional sensitivity analyses were applied for each outcome to assess the robustness of inferences to the matching algorithm. These models included additional patient risk factors, in addition to two markers of prior healthcare utilisation to account for possible differences in health-seeking behaviours between the two groups:A doubly robust model, adjusted for the same covariates included in the matching, plus: smoking status, 12 chronic diseases and the number of A&E attendances and emergency hospital admissions in the year up until 2 weeks before the positive COVID-19 test. In adjusted models, deciles of IMD Score were used rather than the terciles used in matching.A covariate-adjusted model, adjusted for the same variables as the doubly robust model, but without use of matching. This model included all enrolled patients, including those that were not matched in the primary analysis.Further details are given in the cem) command.Analyses were conducted in the Big Data and Analytics Unit Secure Environment, Imperial College. Python V.3.9.5 and Pandas V.1.2.3 were used in data manipulation. Matching was conducted in Stata V.17.0, using the Coarsened Exact Matching  attended ED from 3 days before to 10 days after the positive test. After applying the exclusion criteria, 65 048 patients remained in the analysis, of whom 743 (1.1%) were enrolled to CO@h, and 64 305 (98.9%) were not enrolled . There wOf the eligible cohort, 11 (1.5%) of those enrolled died within 28 days, compared to 1,768 (2.7%) of those not enrolled . In an uOf the 56 793 patients with complete data on sex, ethnicity, IMD and BMI, 658 (1.2%) were enrolled and 56 135 (98.8%) were not enrolled. Of the enrolled patients 639 (97.1%) were matched to 14 982 controls  giving a total of 15 621 in the primary analysis. The characteristics across each of the matching variables of the 19 unmatched and the 639 matched enrolled patients are given in the After matching, patients enrolled had significantly lower odds of 28-day mortality  compared with those not enrolled . In contA sensitivity analysis altering the exclusion criteria for the timeframe from ED attendance to death or admission and a second using only deaths directly caused by COVID-19 or admissions where COVID-19 was listed as a diagnosis produced similar inferences to the primary model . HoweverA doubly robust model adjusted for all matching variables, in addition to presence of chronic conditions, smoking status, and the number of ED attendances and emergency hospital admissions in the previous year. The distributions of the adjusting covariates were similar in the enrolled and control groups for the unmatched variables . Effect In a retrospective matched cohort study of patients with a positive COVID-19 test assessed in ED, those enrolled onto the CO@h programme were found to have 52% lower odds of mortality and  53% lower odds of critical care use within 28 days compared with those who were not on the programme. In contrast, enrolment was associated with a 37% increase in the odds of any subsequent ED attendance and a 59% increase in the odds of any emergency admission within 28 days. The CO@h programme intends to enable early detection of hypoxia, and more timely clinical assessment and hospital admission. It was expected that this might reduce mortality and the need for critical care admission through prompt oxygen therapy, and access to medical treatments shown to reduce mortality and the need for mechanical ventilation.In a separate study of the CO@h programme by the same authors, analysing clinical outcomes across the whole population of people eligible for the programme in England, there was no effect on mortality and small increases in ED attendances and hospital admissions following implementation.et al (2020) found lower odds of ED presentation or re-admission in patients discharged from hospital with remote monitoring, compared with those not enrolled.There is limited pre-existing evidence for the effectiveness of pulse oximetry in health outcomes in patients with COVID-19.Further research is needed to identify other potential benefits and risks of the programme beyond clinical effectiveness and safety, including user experience of both patients and healthcare staff and the cost-effectiveness of the programme. There is also a need to understand equity of the programme, both in terms of access to the programme and whether outcomes vary between different groups of people, which may allow for more effective targeting of the service.a priori and use of different matching variables might impact the findings. However, in the unmatched analysis  seen in the results. Second, in the context of clinical assessment in ED of whether to admit a patient, reassurance provided by a remote monitoring pathway may lower the threshold for ED discharge, leading to the inclusion of a higher severity group in those enrolled.Additional biases in patient selection may impact on the similarity of the two groups, such as through selection of those with greater digital literacy or exclusion of those who were already monitoring their oxygen saturations using personally purchased oximeters. It is possible that health-seeking behaviours varied between groups, and if those enrolled had a lower threshold for presenting to services, this may partly explain the patterns seen. The doubly robust sensitivity analysis adjusted for ED presentations and hospitalisations in the year prior to testing positive for COVID-19 and was consistent with the primary analysis which provides some reassurance that the impact of selection bias here is small. Outcome metrics were also based on all-cause mortality or admissions within 28 days, which may lead to bias if one group had a larger contribution from causes unrelated to COVID-19. However, a sensitivity analysis of deaths or admissions attributable to COVID-19 , indicatThe findings of this study apply to the subset of people with COVID-19 who were reviewed in ED but did not require immediate admission, and who did not die within 1 day of ED attendance. As a result, the findings may not be generalisable to the wider population eligible for the programme, for example, those referred in from primary care, or those presenting at an earlier or later stage of disease. Only 1.2% of the eligible cohort were enrolled, but this should not be viewed as the true enrolment of patients presenting to ED with COVID-19, due to the strict criteria for selection used in this study. The outcome estimates also exclude those with the most severe disease, who were admitted or died within 24 hours of ED attendance and so may not be comparable to estimates from other studies.The CO@h programme is not a homogenous programme, with variation in the type of model implemented, and it is unlikely that a single effect estimate will be representative across all sites.The CO@h programme, implemented in England from November 2020, sought to enable early recognition and intervention for people with COVID-19-induced hypoxia. Among people assessed and discharged from ED, this study found lower odds of mortality and critical care admission and higher odds of subsequent ED attendance and emergency hospital admission in those enrolled compared with those not enrolled to the programme. These findings indicate that for individual patients, pulse oximetry remote monitoring may be an effective pathway to support early detection of hypoxia and escalation of care in patients with COVID-19.