All results generated by the study and the Pockets 2.0 plug-in are available for download.  The exploration of the potential binding cavities within a given target protein represents a key step among the computational tasks, which follow the target identification . After tHence, it comes as no surprise that a remarkable number of computational approaches have been proposed in the last years with a view to mapping the cavities within a given protein structure and to evaluating the resulting druggability. They comprise methods based on Voronoi tessellation , grid seEven though the above-discussed methods to detect protein cavities also include an evaluation of their druggability, other methods are specifically focused on the druggability analysis of the found pockets. They can be based on the physicochemical and structural local properties of the cavities or the comparison with homologous proteins or the sole sequence analysis. Often, these methods combine various kinds of information and develop their predictions by using machine learning approaches .To the best of our knowledge and although very often the detected pockets are further evaluated by performing preliminary docking simulations using reference ligands, the combination of pocket searches with systematic docking analyses as an automatic tool to find and to prioritize the target cavities has never been proposed. Therefore, the present study describes Pockets 2.0, a plug-in implemented in the VEGA ZZ suite of programs , which cBefore applying the here proposed method to identify and to assess potentially druggable binding pockets within the SARS-CoV-2 proteins, a careful literature search was performed. When the binding pockets were not directly identifiable by the available structures, they were derived by comparing the considered SARS-CoV-2 proteins with resolved homologous proteins. This study had two primary objectives: 1) finding putative orthosteric and allosteric pockets by which to assess the performances of the proposed method and 2) finding potential ligands  to be used as probes during docking simulations. Not to mention that the here collected information is relevant per se, since it affords a better understanding of the catalytic mechanism and (if present) of the allosteric modulation for the analyzed enzymes and can guide the corresponding docking simulations. Hence, this section reports the relevant information acquired during this literature analysis for some relevant SARS-CoV-2 proteins. All the information collected during such a comprehensive literature analysis is compiled in The main protease  is a key enzyme involved in viral replication and transcription. Hence, this is a very attractive antiviral target and several resolved structures have been already released. Some resolved structures are in complex with inhibitors thus allowing a precise characterization of its binding cavity. Similarly, screening studies proposed some potential 3CL-PRO inhibitors including both covalent and non-covalent ligands . Here, aThe nucleocapsid protein (N-protein) of Coronaviruses is a structural protein, which packs the RNA genome forming a helical nucleocapsid structure or ribonucleoprotein (RNP) complex . Two X-rThe ADP ribose phosphatase nsp3 of SARS-CoV-2 was crystallized early this year (PDB Id: 6W02) in complex with the substrate ADP ribose, thus allowing a precise characterization of its catalytic cavity. Nsp3 is believed to play key roles in virus replication, which go beyond the simple phosphatase activity, although many of its functions remain to be investigated .Nsp6 is a membrane-spanning protein involved in the compartmentalized viral RNA synthesis . Until nThe nsp9 replicase protein of SARS-CoV-2 was recently resolved (PDB Id: 6W4B). This enzyme is believed to bind RNA-single strand in the viral replication complex ,41. So fNsp12 is an RNA-dependent RNA polymerase (RdRp) and is the main enzyme responsible for the RNA replication of the virus, making it an appealing target for the pharmacological treatment of the SARS-CoV-2 infection. Two structures of SARS-CoV-2 nsp12 in complex with its cofactors nsp7 and nsp8 were recently resolved (PDB Id: 6M71 and 7BV2). As depicted by Saccharomyces Cerevisiae  which bId: 2XZL ). The inNsp14 is a (guanine-N7) methyltransferase (N7-MTase) for mRNA capping. Currently, no resolved structure for SARS-CoV-2 nsp14 is available, but the protein can be modeled by homology techniques as the heterodimer nsp10-nsp14, using the available crystal structures of the SARS-CoV nsp10-nsp14 heterodimer (PDB Id: 5C8S ) as the Nsp15 is a uridylate-specific endoribonuclease, the structure of which was recently resolved in complex with citrate (PDB Id: 6W01 ). The orNsp16 is a 2′ O-methyltransferase  and two The papain-like protease of SARS-CoV-2 (PL-Pro)  was receA last SARS-CoV-2 protein that has attracted great interest in its role in the viral entry is the Spike protein, the receptor-binding domain (RBD) of which is recognized by the human receptor angiotensin-converting enzyme 2 (ACE2) . Hence, Finally and to the best of our knowledge, no binding sites can be identified by analyzing homologous proteins for the non-structural proteins nsp2, nsp4, nsp7-nsp8, and nsp10. Similarly, the membrane protein (M-protein) and the Open Reading Frame (ORF) proteins  are structural proteins for which no druggable site can be found .Such a preliminary analysis allowed the identification of 16 binding pockets within 12 different SARS-CoV-2 proteins: in detail, the orthosteric pocket was identified in all proteins, apart from nsp6 and the Spike protein where the hot spots for the ACE2 interaction were yet detected, an allosteric site was also recognized within nsp6 and nsp13, while at least three allosteric pockets were identified for nsp12. In the following part of the study, the identified binding pockets will be used as a test set to validate the performances of Pockets2.0 by evaluating its capacity to recognize them as the best pocket when ranked by combining pocket and docking searches compared to the two methods taken separately  and/or more than one ligand was considered for each pocket, this benchmarking study involved 40 pocket searches overall.In order to evaluate the correctness of the predicted best pocket, an unbiased approach, which is amenable when analyzing co-crystallized proteins, might be based on rmsd analysis or on volume overlapping between the bound ligand and the pocket shape (as generated by FPocket) or the docked probe molecule (as computed by PLANTS). Unfortunately, the reported analysis involves both theoretical models and resolved proteins with no bound ligands. In these cases, the arrangement of the orthosteric and allosteric binding sites was argued by structural comparison with homologous proteins (as described above) and there are no reference bound ligands for which rmsd or volume overlapping can be evaluated. Hence, the identification of the correct pockets is here based on the identification of some key residues lining the binding site and the detected pockets are evaluated by considering their distance to these key residues. Clearly, the correct pocket is the closest one to the key residues and the binding site is defined as correctly recognized if the closest pocket is ranked as the first pocket . For example, the performances for 3CL-PRO were evaluated by considering the ranking of the detected pocket closest to the catalytic Cys145 residue.As explained under Methods, docking simulations were systematically performed within all found pockets using PLANTS. In order to evaluate the Pockets2.0 capacity to recognize the correct pockets, The analysis of the so obtained performances allows for some meaningful considerations. Above all, the combination of FPocket searches and docking calculations leads to a marked improvement in the number of correctly identified pockets since the consensus score recognizes 30 correct pockets out of 40, with an overall satisfactory precision equal to 0.75. Even though the pocket searches involving theoretical models are markedly lower compared to those on the resolved proteins (7 vs. 33), the obtained results do not seem to be influenced by the source of the protein structure since the correct cavity was ranked as best pocket within theoretical models in four cases out of seven.In detail, FPocket alone was able to recognize one half of the correct pockets , a performance which is in line with that reported by the above mentioned benchmarking study for FPocket when used without repetitions . The perInterestingly, the consensus score clearly reduces the number of cavities that are incorrectly ranked as second or third cavity compared to the Fpocket or docking scores alone. This indicates that the combination of the two scores allows the pockets that were incorrectly ranked by the two search methods alone to be promoted as the best pocket. In detail, the analysis of the 30 pockets correctly identified by the consensus score reveals that only in 11 cases (out of 11), the correct pockets were ranked as the best cavity by both methods, in 13 cases (out of 16), the correct pockets were ranked as non-best solutions by one method, and in 6 cases (out of 13), the correct pockets were ranked as non-best solutions by both methods. These results reveal that in 19 cases (out of 29), the combination of both methods played a crucial role in identifying the correct pocket, thus emphasizing the remarkable potential of the here proposed combined approach. Intriguingly, the above reported results also allow for another interesting interpretation since they reveal that the correct cavity is successfully recognized in 100% of the cases when both methods ranked it as the best solution. The probability of recognizing the correct cavity decreases to 81% if at least one method ranked it as the best pocket, dropping to 46% if both methods are unable to recognize it as the best solution. These results suggest that the combination of pocket and docking searches affords a mutual validation, which increases the reliability of the predicted pockets.The validation of the here proposed method can be also assessed by considering that the performed pocket searches involved a total of 1364 cavities, which can be subdivided into 40 correct pockets and 1324 incorrect pockets. This means analyzing confusion matrices including a) 20, 18 and 30 true positives; b) 20, 22 and 10 false positives and false negatives and c) 1304, 1302 and 1314 true negatives for Fpocket, PLANTS and consensus rankings, respectively. While considering such an unbalanced dataset, the increase in the MCC value as obtained by combining pocket and docking search (from 0.48 to 0.74) affords a further confirmation of the potential of the here proposed approach.A detailed analysis of the investigated proteins reveals that the pocket search for the 14 resolved 3CL-PRO structures is substantially driven by FPocket, which indeed recognizes the correct pocket in 11 cases. These notable FPocket performances can be here explained by considering that its score, while also including parameters related to the local features of the pockets, tends to favor the large pockets. While considering a certain degree of heterogeneity among the reported pocket volumes, which is explainable by considering that even small changes in the arrangement of the lining side chains can induce a different pocket fragmentation by FPocket, the 3CL-PRO catalytic cavity is by far the largest pocket and thus is almost always correctly recognized by FPocket. When discarding the 3CL-PRO structures, the results markedly change since FPocket is able to recognize the correct pocket only in 9 cases out of 26, and the pocket search appears to largely benefit from docking simulations as evidenced by the fact that consensus score correctly recognizes 18 pockets out of 26. These last results emphasize how the combination of pocket and docking searches could be particularly effective when exploring complex proteins endowed with narrow binding cavities, and indeed it appears truly productive when looking for allosteric binding sites, as seen for nsp6 and nsp12.The analysis of the pockets for nsp12 and the Spike protein deserves a separate description. With regard to the nsp12 allosteric cavities, the two cavities on the thumb region can be identified with difficulty due to the folding of some loops, which partly occupy the cavities, thus leading to their unsatisfactory fragmentation. Hence, the cavity n. 2 was selected since it is large enough to encompass both allosteric sites while not matching either one specifically. Again, the allosteric cavity on the palm region is merged to the orthosteric site, thus generating a unique very large binding site (pocket n. 1), which can suggest the design of bivalent ligands able to interact with both subpockets.Concerning the Spike protein, all simulations involved the resolved complexes of Spike–ACE2 even though the pockets detected on the ACE2 surface were discarded from the analyses. Though to test the Pockets 2.0 performances, the analysis of the largest Spike–ACE2 complex (PDB Id: 6M17), a hexamer that also includes two chains of the sodium-dependent neutral amino acid transporter B(0)AT1, was also repeated by considering all 275 detected pockets. Gratifyingly, FPocket ranks the correct pocket in the eleventh position while docking simulations proved successful to recognize the correct pocket even in such a challenging situation, and the pocket was ranked as the second one by consensus score, thus further confirming the key role of docking simulations to identify the druggable cavities.With regard to the badly predicted pockets, there are only two cases in which the correct pocket is ranked off the podium, namely the allosteric site of nsp13 and the orthosteric cavity of the nsp10-14 for the SAH ligand. The unsatisfactory prediction of the nsp13 allosteric pocket can be justified by considering that this binding site should be located at the interface between nsp13 and nsp12 (see above), and thus cannot be properly mapped by considering only nsp13. Again, the wrong prediction for the nsp10-14 heteromer is due to unsuitable docking results, which can be rationalized by considering that SAH is in fact the reaction product and indeed the performances significantly enhance when considering SAM, which is the true cofactor.https://swissmodel.expasy.org/repository/species/2697049). Homology modelling (HM) was performed by SWISS-MODEL [https://modelarchive.org/doi/10.5452/ma-epkbe for nsp13 and https://modelarchive.org/doi/10.5452/ma-3rg44 for the nsp10-14 heteromer. De novo models (DN) for nsp6 were obtained from the Feig group (https://github.com/feiglab/sars-cov-2-proteins) [Available experimental structures were downloaded from the PDB  eneSS-MODEL  with theSS-MODEL . The gloSS-MODEL  and QMEASS-MODEL . The homroteins) . Specifi-1Å-1 (maximum number of steps = 10000) with the CHARMM force field and the Gasteiger’s atomic charges. All tested probe ligands were optimized by PM7 semi-empirical calculations [Be they resolved or modelled, all simulated proteins were prepared by adding (when necessary) the hydrogen atoms to remain compatible at physiological pH and by removing (when present) water solvents, crystallization additives, and bound ligands. The protein structures were then minimized by using Namd2   and by kulations . When neulations  and focuStarting from release 2.4, the VEGA ZZ suite of programs  comprises a graphical interface to FPocket (named Pocket plug-in) . FPocketOnce the pockets are calculated and analyzed, they can undergo the docking simulations. To do this, the selected ligand(s) should be suitably prepared depending on the chosen docking program. After setting the docking parameters, the simulations are automatically performed on all selected pockets using all collected probe ligands. Based on all computed scores, user-defined consensus functions can be calculated to afford a combined ranking of all cavities. In the here described analysis of the SARS-CoV-2 proteins, the consensus score is computed by summing the ranks obtained by sorting the overall FPocket scores and the ChemPLP docking scores from best to worst.Along with the interactive inspection of the found pockets and the corresponding ligand poses within the VEGA ZZ graphical interface, the Pockets 2.0 plug-in generates a set of additional output files regarding the computed poses for each pocket and the corresponding score values.For the here performed analyses, the pocket search was performed by applying the default settings of FPocket  with the only exception of the minimum number of spheres to define a pocket that was set equal to 30 (instead of 35) for a more exhaustive analysis even of the small cavities. For each protein, all found cavities underwent docking simulations using PLANTS. In detail, the search was focused within the corresponding spheres as derived from FPocket analysis by increasing their radius of 3 Å to completely encompass the entire binding cavity. For each simulated ligand and each explored cavity, one pose was generated by using the PLANTS_ChemPLP scoring function with speed equal to 1. In all the analyses, the consensus function of each pocket was computed by combining the overall FPocket score with the ChemPLP score. When more than one ligand was used as a probe for a pocket, the consensus score involves the average of the corresponding ChemPLP scores. Since almost all the analyzed SARS-CoV-2 proteins are enzymes with various catalytic activities, the performances of the here presented combined approach was preliminarily assessed, also considering a set of resolved GPCRs, the evaluation of which was reported in the The study had two primary objectives: characterizing the druggable binding sites within the therapeutically relevant SARS-CoV-2 proteins as well as presenting and validating a novel strategy to search and prioritize the protein pockets by using the identified SARS-CoV-2 sites as a test set. Concerning the first objective, an extensive literature search combined with the comparison with homologous proteins allowed precise identification of 16 druggable pockets within 12 different SARS-CoV-2 proteins, including four allosteric sites. In this context, nsp12 represents a very interesting target since its homology with the corresponding HCV enzymes allowed the identification of at least three allosteric pockets, which can play key roles in determining the nsp12 activity as well as its interactions with the cofactor proteins.Concerning the second objective, the study reports the Pockets 2.0 plug-in as implemented in the VEGA ZZ suite of programs, which automatically combines the pocket search, as performed by FPocket, with docking simulations by using representative probe ligands and PLANTS or AutoDock/Vina as docking engines. The combination of the FPocket and docking scores by calculating customizable consensus scores leads to a significant increase in the correctly identified binding sites compared to the FPocket and docking scores alone, and this enhancement appears to be truly relevant when considering the MCC values which, while considering the dataset unbalancing, show an increase of 35% when combining pocket and docking searches.Again, an ancillary analysis involving an extended set of aminergic resolved GPCRs see  revealedhttp://www.exscalate4cov.network. The repository will be periodically updated by including the pocket mapping for the new available target proteins. The homology models have been deposited in the ModelArchive database with the unique stable accession codes (DOI) https://modelarchive.org/doi/10.5452/ma-epkbe for nsp13 and https://modelarchive.org/doi/10.5452/ma-3rg44 for the nsp10-14 heteromer. The Pockets 2.0 plug-in is freely available within the VEGA ZZ suite of programs at http://www.vegazz.net.All prepared protein and ligand structures, as well as all files generated by Pockets 2.0 searches, are available at


One hundred years ago, Otto Heinrich Warburg observed that postmitotic retinal cells are the highest oxygen-consuming cells in the body. He compared these cells to actively growing mitotic tumor cells since both cells reprogram glucose for anabolic processes, which include lipid, protein, and RNA/DNA synthesis, and for antioxidant metabolism. To achieve this metabolic reprogramming, cancer cells preferentially express a less active dimeric form, the M2 isoform of pyruvate kinase (PKM2), which shuttles glucose toward the accumulation of glycolytic intermediates that redirect cell activities into anabolic processes. Similar to cancer cells, retinal photoreceptors predominantly express the M2 isoform of PKM2. This isoform performs both metabolic and non-metabolic functions in photoreceptor cells. This review focuses on the metabolic and non-metabolic roles of pyruvate kinases in photoreceptor cell functions. Almost 100 years ago, German physiologist Otto Heinrich Warburg noticed that retina consumes the highest amount of oxygen in the body; he compared this oxygen consumption to that of a rapidly dividing cancer cell ,b. The Wcis-retinal is isomerized to all-trans-retinal; NADPH is an absolute requirement for the reduction of all-trans-retinal by the enzyme retinol dehydrogenase 8 (RDH8)  cycle. Photooxidation products generated through rhodopsin activation generate reactive oxygen species (ROS), which are toxic to the photoreceptor cell . The pen8 (RDH8) .trans-retinal as a means to detoxify this retinoid.Checkpoints in the glycolytic pathway and shunting the glucose to the PPP to generate NADPH and ribose are evolutionarily established . GlucoseAlthough it was a century ago that Warburg noticed that tumor cells and retinal cells redirect glucose for anabolic processes, the mechanism behind the fuel redirection from oxidative phosphorylation to anabolic processes was not known until 2011. Investigators for the first time identified that higher pyruvate kinase enzyme activity results in the release of ROS during yeast respiration . These iAnother landmark observation made by Dr. Lewis Cantley, that pyruvate kinase M2 isoform is a phosphotyrosine binding protein , suggestPyruvate kinase is a known glycolytic enzyme involved in the last step of glycolysis by converting PEP to pyruvate. Four isoforms of pyruvate kinase have been identified: PKM1, PKM2, PKR, and PKL . More thThe PKM1 and PKM2 isoforms arise from a single gene as alternatively spliced products . The PKMPKM1 and PKM2 differ in only 22 amino acids in their sequences, but the functions mediated by PKM1 and PKM2 are distinct. Interestingly, PKM2 has >95% identity and >98% similarity in sequence with PKM1. The crystal structure of human PKM1 and PKM2 has been solved . We carried out the molecular modeling of mouse PKM1 and PKM2 using the Raptor X template-based protein structure modeling web server  and aligThese differences between PKM1 and PKM2 seem to be localized to a part of the protein which appears key for dimerization and/or tetramerization indicating these changes might affect the substrate specificity based on the extent oligomerization . PKM1 alPrevious studies showed that substitution of PKM2 in tumors with PKM1 reverses the cancer phenotype . These eThe role of PKM1 in tumor progression is controversial. More recently, PKM1 has been shown to promote the growth of multiple tumor lines . These sPKM2 is known to regulate glycolytic activity and anabolic processes . BesidesThe retina is a post-mitotic neuronal tissue consisting of seven layers of cells, including rod and cone photoreceptor cells. These cells are metabolic, and their energy expenditure is almost equivalent to that of a cancer cell. Glycolysis is indispensable for photoreceptor cell survival; ablation of this pathway resulted in retinal degeneration , whereasThe retina expresses both PKM1 and PKM2. However, the expression is cell-specific. PKM1 is predominantly expressed in the inner plexiform layer and ganglion cell layer, and weakly expressed in the rod inner segments . PKM2 ishttps://proteinpaint.stjude.org/F/2019.retina.scRNA.html. From this publicly available database, we searched the pyruvate kinase (Pkm) gene expression and found that it is expressed differently in various cell types of the retina  and GTPase-activating protein, regulators of G-protein signaling 9 (RGS9) . Increasin vitro  residue on human PGAM1. This reaction has been shown to occur with physiological concentrations of PEP and produced pyruvate in the absence of PKM2 activity. Pyruvate can also be produced through amino acid metabolism , reducing the anabolic activity and redirect the pyruvate to fuel mitochondria for oxidative phosphorylation may prevent retinal cell death and promote retinal cell survival.The PKM2 isoform is expressed in both rod and cone photoreceptors , but conAnother possibility is the β-oxidation of fatty acid-generated acetate in the mitochondria. A 2016 study reported that photoreceptors utilize both fatty acids and glucose for ATP production, and defects in the fatty acid transport pathway were shown to cause AMD . If thisin vivo (Based on this published work, we can hypothesize that some of the fatty acids recycled to inner segments from the RPE come from the choroidal circulation, and the inner segments may receive more fatty acids than are needed for membrane synthesis. The remaining fatty acids could be oxidized in the mitochondria to generate acetate to fuel the TCA cycle. Nevertheless, it has been shown that the n3 PUFAs is an indispensable constituent of outer segment membranes . If thesin vivo , suggesttrans-retinal to all-trans-retinol, reducing oxidized glutathione, and providing reducing equivalents for lipid and protein synthesis. The key to understanding in PKM2 knockout mice retinas and how these mice survived and the source of energy production in these retinas remain open questions.We must keep in mind that we are dealing with a chronic, steady-state adaptive response to ablation of a gene, rather than an acute response to events that may have occurred over a matter of days. Thus, we are measuring responses caused by the long-term loss of the normal source of pyruvate for the TCA cycle’s production of ATP. In the retina, NADPH serves three important functions: reducing all-Studies in the retina show that aerobic glycolysis is essential for retinal cell survival. Although photoreceptors are post-mitotic, PKM2 is the predominant isoform in these cells. In tumor cells, the substitution of PKM2 with PKM1 reversed the tumor phenotype. In photoreceptor cells, the deletion of PKM2 upregulates PKM1, yet it fails to clear the accumulation of glycolytic intermediates. Studies of retinal photoreceptor cells show that PKM2 is required for rod and cone photoreceptor function and survival and that PKM2 regulates photoreceptor cells both metabolically and non-metabolically.The author confirms being the sole contributor of this work and has approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


P-value >0.05). Case-to-undetected-infections ratio was 1:195 and infection fatality rate was calculated as 2.94 per 10 000 infections. We estimated seroprevalence of COVID-19 was very high in our study population. The focus in this slum should shift from infection prevention to managing the indirect consequences of the pandemic. We recommend seroprevalence studies in such settings before vaccination to identify the vulnerability of COVID-19 infection to optimise the use of insufficient resources. It is a wake-up call to societies and nations, to dedicate paramount attention to slums into recovery and beyond – to build, restore and maintain health equity for the ‘Health and wellbeing of all’.People living in urban slums or informal settlements are among the most vulnerable communities, highly susceptible to coronavirus disease 2019 (COVID-19) infection and vulnerable to the consequences of the measures taken to control the spread of the virus. Fear and stigma related to infection, mistrust between officials and the population, the often-asymptomatic nature of the disease is likely to lead to under-reporting. We conducted a cross-sectional study to determine the seroprevalence of COVID-19 infection in a large slum in South India 3 months after the index case and recruited 499 adults (age >18 years). The majority (74.3%) were females and about one-third of the population reported comorbidities. The overall seroprevalence of IgG antibody for COVID-19 was 57.9% (95% CI 53.4–62.3). Age, education, occupation and the presence of reported comorbidities were not associated with seroprevalence ( Coronavirus disease 2019 (COVID-19) is a pandemic of historic significance characterised by its ubiquitous presence, accelerated expansion and catastrophic economic consequences. Globally, over a few months of the pandemic outbreak, India became one of the epicentres of this contagion, contributing to one-sixth of the world's reported cases [2 with an estimated population of 100 000 people [Devarajeevanahalli , also known as the ‘Dharavi of Bangalore’ is one of the largest governments notified slums in Bangalore, extending over 1.15 km0 people –5 , each of which was further divided into four clusters for sampling purposes. A kiosk was set up in one of the trusted community spaces in each cluster. Our community health workers invited people (adults ⩾18 years) from houses to give blood samples. If a household refused to participate, then the next house was approached. In each cluster, mobilisation continued till the desired sample size was achieved. We explained the purpose of the study, took written consent and interviewed people with a questionnaire. The questionnaire contained questions about demographic information , history of exposure to COVID-19 infection , any history of COVID-19-related symptoms a month before the survey and their self-reported compliance to handwashing and use of face masks. Our Phlebotomists collected 5 ml of blood from each participant via venepuncture in a plain vacutainer and transported to BBH laboratory  and NABL  accredited) within 5 h maintaining the cold chain.The serum was separated and used to test for antibodies using the Elecsys Anti SARS CoV2 assay, an electrochemiluminescent immunoassay using a recombinant protein representing the nucleocapsid (N) antigen for the determination of high-affinity antibodies including IgG against SARS CoV2 . This asThe frequency of characteristics of the survey participants was described. Seroprevalence of COVID-19 IgG antibody was reported in per cent with 95% confidence interval (CI). Case-to-undetected-infections ratio (CIR), was calculated as a ratio of the number of reported quantitative real-time polymerase chain reaction (RT-qPCR)-confirmed COVID-19 cases, 2 weeks before the initiation of serosurvey (IgG antibodies against SARS-CoV-2 infection start appearing by the end of the first week after symptom onset and most cases are IgG positive by the end of the second week) to the number of people who have IgG antibodies . AssuminThe Ethics Committee on Bangalore Baptist Hospital approved the survey protocol on 30 June 2020. Written informed consent was obtained from the participants and the test results were communicated to them.Our 499 participants were equally distributed in both wards (0.53% and 0.47%). The mean age was 39.7 + / 14.5 years and the majority (74.3%) were females. Most people (96.9%) had less than 12 years of education and 56.5% did not have any job. About one-third of the population reported comorbidities see . HyperteThe overall seroprevalence of IgG antibody for COVID-19 was 57.9% (95% CI 53.4–62.3) . When weP-value >0.05) whereas female gender was significantly associated with seroprevalence.Age, education, occupation, presence of comorbidities and presence of self-reported symptoms were not associated with seroprevalence , Iran (0.08–0.12%), Brazil and Spain (1%) and of India in May –26. ThisA previous study in the same slum noted a higher prevalence of hypertension (35.5%) and diabetes (16.6%) . In thatThe study participants reported improvement in hygiene measures like hand sanitation and frequent use of face covers. This may be due to improved behaviour following COVID-19 awareness campaigns led by the government and civic bodies and mandatory workplace enforcement. The possibility of social desirability bias (a discordance was noted as many were not wearing masks though they labelled themselves as frequent mask users during the survey) cannot be ruled out in this setting.What does this ‘high seroprevalence’ mean to us? First and foremost, it suggests that the worst is over. It is a relief to realise that the virus spared most lives in this slum. With more than half of the population already being infected, the infection curve is likely to have started its journey down and COVID-19 will cease to be a public health problem in DJ Halli. This is confirmed by a low COVID-19 test positivity rate (1.5–2%) despite adequate testing (2000 tests per million/day) from this slum in the past one and half months (unpublished data from the government). Likewise, we also did not see any suspected cases from our DJ Halli clinic in the past 2 months. These findings provoke us to rethink the need for a vaccine in this slum. If half the population have antibodies, should we still call them vulnerable to COVID-19? Is any vaccine a match for natural infection in these settings? This study is not giving easy answers, but it does give rise to such questions.Second, the findings suggest it is time to shift our strategy from chasing the virus' to ‘mending the damages’; damages caused by other diseases when COVID-19 annexed our undivided attention and the damage caused by the loss of livelihoods. During the pandemic, the entire health system was reorganised to support to pandemic response leading to scaling down and suspension of disease control programmes, immunisation and primary health care services. Due to fear, people postponed their hospital visits for their non-communicable diseases and other life-threatening conditions. Loss of jobs has aggravated hunger and nutritional deprivation. In a survey by the WHO among 105 countries around the globe, almost every country (90%) experienced a disruption to some extent, with greater disruptions being reported in low- and middle-income than in high-income countries in essential health services across the life course . We needThe pandemic was clearly a catch-22 situation in slums, where, if people continued their life, as usual, they faced the risk of getting infected. If they stayed at home as was being directed, they lost their livelihoods and sources of sustenance. Hence, we shall have to find ways to keep providing ‘regular’ healthcare to prevent the consequences of trying to control the virus being more damaging than the virus itself. This is especially relevant in settings like this slum, where it is practically impossible to prevent the spread of the virus.The pandemic has also brought mercies. Slums have never experienced so much attention and care as during this pandemic. Every city highlighted their slums on their maps; we witnessed ‘missing millions’ becoming the ‘pinnacle of attention’. Government and civic bodies formed alliances, crafted strategies, pooled in resources to serve slums, thus attaining unbelievable progress on many domains in the past 3 months .The possibility of another pandemic and the vulnerability of slums are stark realities in front of us. Had the mortality been higher, the contagion would have wiped millions and the slums would have been the reservoirs. Hence, we should take the lessons from this pandemic seriously. The first step would be recognising the slums and their inhabitants by improving basic living conditions, facilitating stable economic inclusion and promoting access to quality education and health services. Such investments in slums will reap huge dividends due to neighbourhood effects . So, theThe study may have some potential biases. We have selected different locations in the specific slum and then people were invited for the study. This sampling might have led to selection bias which could have impacted the true prevalence estimation. However, we were not able to assess the impact of this on the prevalence. Measurement bias could be another possibility due to validity parameters of the test used, however, we attempted to overcome this by reporting test performance adjusted seroprevalence rate with CI.This is the first study reporting seroprevalence from a slum in South India and the second study from India. It corroborates the findings of the first study (peak attained within 3 months of index case) and strengthens the body of evidence related to one of the most vulnerable populations. It also gives guidance for the planners on allocating resources judiciously between COVID and non-COVID care in the slums of India which harbour more than 65 million people.The study had few limitations. Firstly, the sampling strategy emphasised more on pragmatism than representativeness in the context of the communal violence which erupted in DJ Halli 2 weeks before the survey. Secondly, we have presented unadjusted seroprevalence rate as there was no data available on the age-sex distribution of this population. However, the estimates are very likely to be close to the real estimate as the prevalence was almost similar in all the strata. Thirdly, our sample had a smaller number of men compared to women as they were not available at home during the daytime. This might have resulted in an underestimation of seroprevalence as men have more social contacts than women in this context . FourthlThe study in a dense slum in South India after 3 months of index case showed a high seroprevalence of COVID-19 infection in this setting. For every case reported, there were 195 undetected cases, which unearths the implication of the often-asymptomatic nature of the disease in reflecting the true count of people with COVID-19 infection. Vulnerability to infection is the primary condition to assign priority for COVID-19 vaccination, due to vaccine demand exceeding our existing capacity of vaccine production. Hence, we should keep in mind the possibility of most vulnerable communities achieving immunity with natural infection, thus negating the benefit of the vaccine in this population. The process of carrying out a seroprevalence study in dense settlements before vaccination may prove advantageous in identifying the disadvantaged communities which will benefit most from vaccination.It is vital to realise that containing an infectious respiratory virus was practically impossible in slums, even with the best of efforts from all sectors. Slums received attention and coordinated efforts from government and civic societies during this pandemic, which is containing the damage caused by the pandemic. We should nurture these networks beyond the pandemic to strengthen health security of slums and their inhabitants.Since the infection is controlled in slums like DJ Halli, what is the way forward – ‘hands-off’ or ‘all hands on the deck approach?’ The answer lies in the wisdom – that the debate should not only be about the virus but more importantly, about the people living in slums!


ACADVL, TRABD, and VASP) encode proteins that are upregulated in activated microglia in AD.Microglial dysfunction has been proposed as one of the many cellular mechanisms that can contribute to the development of Alzheimer’s disease (AD). Here, using a transcriptional network map of the human frontal cortex, we identify five modules of co-expressed genes related to microglia and assess their role in the neuropathologic features of AD in 540 subjects from two cohort studies of brain aging. Two of these transcriptional programs—modules 113 and 114—relate to the accumulation of β-amyloid, while module 5 relates to tau pathology. We replicate these associations in brain epigenomic data and in two independent datasets. In terms of tau, we propose that module 5, a marker of activated microglia, may lead to tau accumulation and subsequent cognitive decline. We validate our model further by showing that three representative module 5 genes ( The amount of these two pathologies that are present in the older brain is strongly but only partially correlated2, enabling us to distinguish molecular pathways that are involved in one or the other process of protein aggregation. While genome-wide association studies (GWAS) have unequivocally pointed to the innate immune system and particularly myeloid cells as major contributors to AD pathophysiology5, our current understanding of the mechanistic involvement of microglia and infiltrating macrophages in human AD pathology is rudimentary. Much of what shaped our understanding of the role of innate immune cells in AD pathogenesis is based on studies performed in animal models of AD which imperfectly capture the many different aspects of the human disease6. In addition, significant differences have been reported between mouse and human innate immune responses in aging7 and AD8. Human genetic, translational, and imaging studies have implicated myeloid cells in all aspects of AD, from the asymptomatic phase of amyloid accumulation10 to the progression of dementia12, but there is little clarity on how disparate observations can be assembled into a coherent picture. Therefore, to identify the different components of human myeloid responses that exist in the neocortex and to examine how each component contributes to the continuum of AD-related pathological and clinical traits in the aging brain, we evaluated a recently derived Molecular Network Map of the aging human frontal cortex using tissue level RNA sequence (RNA-seq) data obtained from the frozen dorsolateral prefrontal cortex of participants in two prospective studies of cognitive aging, the Religious Order Study (ROS) and the Memory Aging Project (MAP)15. From the RNA-seq data, we derive groups of co-expressed genes—that we term “modules”—and build the network from these modules as well as outcome measures available from each of the 540 participants that were profiled. As these individuals are non-demented at study entry, they represent a sampling of the older, aging population. At the time of death, they display a full spectrum of clinicopathologic features related to AD that are found in older people including cognitive decline, dementia, extracellular β-amyloid deposition, hyperphosphorylation of Tau, and microglial activation16.Alzheimer’s disease (AD) is characterized pathologically by the accumulation of both β-amyloid and Tau pathologies which lead to the gradual loss of cognitive function and, ultimately, dementia17, we identify five microglia-related modules of co-expressed cortical genes that capture different transcriptional programs of microglia. We focus on dissecting the role of these modules in AD; in mapping the conditional relationship between modules and cognitive and neuropathologic outcomes, we focus on the identification and histologic validation of a microglial module that contributes to the accumulation of Tau pathology. Two other modules relate to β-amyloid, and a fourth—enriched for AD susceptibility genes—appears to be primarily related to aging. Thus, we provide an initial immune network perspective of the divergent sets of co-expressed genes that govern microglial identity in the aging human brain and identify the relationship of these components to specific pathologies and clinical symptoms.Here, using our recently established gene expression signature of aged human microglia (HuMi_Aged geneset)13 and the Memory and Aging Project (MAP)14 which are designed to be merged for joint analyses. These studies enroll non-demented individuals and include detailed, annual antemortem characterization of each subject’s cognitive status as well as prospective brain collection and a structured neuropathologic examination at the time of death. The study design of ROS and MAP yields an autopsy sample that includes a range of syndromic diagnoses and neuropathologic findings that are common in the older population. All brain autopsies, experiments, and data analysis were done in compliance with protocols approved by the Partners Human Research Committee or the Rush University Institutional Review Board. The subjects in the study have an average age of 88, 61% meet criteria for pathologic AD by NIA-Reagan criteria18 and 64% are female.The subjects profiled in this study are participants in one of two prospective cohort studies of aging, the Religious Orders Study (ROS)19 with poly-A selection20. Sequencing was performed on the Illumina HiSeq with 101 bp paired-end reads and achieved coverage of 150 M reads of the first 12 samples. These 12 samples will serve as a deep coverage reference and included 2 males and 2 females of non-impaired, mild cognitive impaired, and Alzheimer’s cases. The remaining samples were sequenced with a target coverage of 50 M reads. The libraries were constructed and pooled according to the RIN scores such that similar RIN scores would be pooled together. Varying RIN scores result in a larger spread of insert sizes during library construction and lead to uneven coverage distribution throughout the pool.RNA was sequenced from the gray matter of dorsal lateral prefrontal cortex (DLPFC) of 542 samples, corresponding to 540 unique brains. These samples were extracted using Qiagen’s miRNeasey mini kit (cat. no. 217004) and the RNase free DNase Set (cat. no. 79254). RNA was quantified using Nanodrop. Quality of RNA was evaluated by the Agilent Bioanalyzer. All samples were chosen to pass two initial quality filters: RNA integrity (RIN) score >5 and quantity threshold of 5 µg (and were selected from a larger set of 724 samples). RNA-Seq library preparation was performed using the strand-specific dUTP method21, and RSEM was used to estimate expression levels for all transcripts22. The FPKM values were the outcome of our data RNA-Seq pipeline. Data are available on https://www.synapse.org/#!Synapse:syn3388564.The RNA-Seq data were processed by our parallelized pipeline. This pipeline includes trimming the beginning and ending bases from each read, identifying and trimming adapter sequences from reads, detecting and removing rRNA reads, and aligning reads to reference genome. The non-gapped aligner Bowtie was used to align reads to the transcriptome reference23 to remove potential batch effects. Expression levels were quantified for 55,889 unique genes. We placed a threshold for expression, only keeping 13,153 genes with average FPKM greater than one. For the creation of the modules as previously reported15, we used linear regression (on log2 expression data) to remove the effect of major biological and technical confounding factors on a per-gene basis. Biological confounding factors include three genotyping PCs (to represent ancestry), age at death, and sex. Technical confounding factors include RIN, number of ribosomal bases, number of aligned reads, study index (ROS or MAP), and postmortem interval. For the module, pathological association analysis and comparisons between microglia profiles and bulk expression, GC, and length bias effects were first removed using a smoothed trimmed means of M-values before genes were averaged within each module.For normalization, we first applied quantile normalization to the FPKM values and then used the combat algorithm24. Briefly, β-amyloid was labeled with an N-terminus-directed monoclonal antibody . Immunohistochemistry was performed using diaminobenzidine as the reporter, with 2.5% nickel sulfate to enhance immunoreaction product contrast. Between 20 and 90, video images of stained sections were sampled and processed to determine the average percent area positive for β-amyloid  of PHFtau tangles  and anti-human HLA-DP, DQ, DR antibodies  using standard Bond epitope retrieval and detection. An investigator blinded to the clinical and pathologic data, outlined the cortical gray matter region of interest on each slide using a Microbrightfield Stereology System. The Stereo Investigator 8.0 software program was used to place a 1000 × 750-μm sampling grid over the region and the program was engaged to sample 4.0% of the region with a 200 × 150-μm counting frame at ×400 magnification at interval grid intersection points. Using separate tags for stages 1, 2, and 3 microglia, the operator marked the microglia at each intersection point. These counts were then upweighted by the stereology software to estimate a total number of microglia  in the defined area. This approach relies on the fact that different stages of microglia activation from least (stage 1) to most (stage 3) activated can be defined based on their cellular morphology; when microglia become activated, their long fine processes contract and thicken and the cell body adopts a larger more rounded cellular conformation.Microglia morphologies had previously been measured with immunohistochemistry for 105 ROSMAP subjects in our studyTMEM119 (Sigma Aldrich) and VASP (Santa Cruz Biotech). The sections were blocked with a blocking medium (8% of horse serum and 3% of BSA) and incubated overnight at 4 °C with primary antibodies. Sections were washed with PBS and incubated with fluorochrome-conjugated secondary antibodies (Thermo Fisher) and coverslipped with an anti-fading reagent containing Dapi . Photomicrographs were captured at X20 magnification using Zeiss Axio Observer.Z1 fluorescence microscope and exported to Image J imaging software . For each subject, 30 images were taken in a zigzag sequence along the cortical ribbon to ensure that all cortical layers were represented in the quantification. This analysis was repeated for eight subjects from New York Brain Bank at Columbia University where 10-μm sections of frozen tissue from the DLPFC were used to co-stain CD45 (Fisher) with TRABD (Sigma) or ACADVL (Sigma). Phosphorylated tau protein (pTau)  were stained in sister sections. After fixation with methanol or ethanol for 15 min at −20 °C, tissues were blocked with blocking medium for one hour then incubated with primary antibodies overnight at 4 °C. After washing with PBS, tissues are incubated with fluorochrome-conjugated secondary antibodies and mounted with mounting media containing DAPI. Between sister sections, images from the same region have been captured with the same approaches as previously described. Levels of pTau were measured as the proportion (%) of the stained area related to the total are of the images.For six subjects from the clinical core at The Rush Alzheimer’s Disease Center, 6-μm sections of formalin-fixed paraffin-embedded tissue from the DLPFC were used to stain 17, an aged human microglia signature (HuMi_Aged geneset) of 1030 microglia-enriched genes were defined by comparing the expression of genes in DLPFC isolated microglia to their expression level in the bulk DLPFC tissue. The identified genes had fourfold larger expression in the microglia samples relative to the tissue level samples and average FPKM greater than one in the microglia samples.As previously reported15, the SpeakEasy algorithm30 was used to derive gene modules from normalized gene expression data. Consensus clustering results from 100 initializations of the SE algorithm yielded 257 modules, 47 of which contained at least 20 gene members (meaning modules are assigned for 98% of genes) and were examined in downstream analyses. Pseudo-expression values for each module were calculated by taking the mean expression level of all genes assigned to that module after the expression data have been standardized for each gene. The gene modules that we used in this paper are the same as those defined in our prior manuscript15 which focused on discovering driver genes relating to module 109.As previously reported32. For both datasets, gene counts were TMM normalized and voom33 was used to detect microglia-specific genes relative to all other cell types using a Bonferroni adjusted P value of 0.05. Our isolated microglia profiles were also compared to our reference RNA-seq profiles of human iPSC-derived neurons and primary human astrocytes (https://www.synapse.org/#!Synapse:syn2580853/wiki/409844) to identify genes whose expression was enriched in microglia, astrocytes or neurons. The microglia-enriched genes were genes that had at least a fourfold increase in expression relative to both the astrocyte profile and the neuron profile. The astrocyte and neuron enriched genes were defined in the same way. We also confirmed cell-type enrichment using cell-type-specific profiles from the mouse cortex tissue34 dataset. Using these cell-type-specific gene sets, we then explored the relative expression of the genes within each module in the microglia-astrocyte-neuron gene expression space using ternary plots.Hypergeometric tests were used to test whether modules contained more microglia-related genes than expected by chance. All 13,153 genes that had an FPKM value greater than 1 in the DLPFC ROSMAP data were used as the background for the tests. The HuMI_Aged geneset was used as the primary reference for these analyses. Reference sets derived from two other manuscripts were also used for validation35. Only biological processes with more than 20 annotated genes or less than 500 genes were tested. Only genes that were assigned to a module and in at least one biological process were included in the hypergeometric tests.Hypergeometric tests were used to test which Gene Ontology biological process demonstrated an excess in overlap, over what is expected by chance, with each of the five microglial modulesHypergeometric tests were used to test if the targets of any transcription factors demonstrated an excess in overlap, over what is expected by chance, with each of the five microglial modules. Transcription factor target genes were downloaded from MSigDB from the C3:regulatory target gene sets:TFT: transcription factor targets. Only genes that were assigned to a module and targeted by at least one transcription factor were included in the hypergeometric tests.P < 0.05) in IGAP36. Only genes that were assigned to a module and contained at least one IGAP probe were included in the tests. The INRICH37 GUI v1.0 was also used with default settings to perform enrichment analysis.Hypergeometric tests were used to test whether modules contained more genes associated with Alzheimer’s disease than expected by chance. A gene was said to be associated with Alzheimer’s disease if it had at least one probe that was 50 KB up or downstream of the gene that was nominally significant  and the square root of the numbers of neuritic plaques (NP), neurofibrillary tangles (NFT), the amount of β-amyloid or tau and. All the associations were adjusted for age, sex, study (ROS or MAP), RIN, and postmortem interval (PMI). A similar analysis was performed including the average expression of neuronal (m21), astrocyte (m107), oligodendrocytes (m110), and endothelial cell (m112) modules in the model to account for major shifts in cell-type proportions.4. Data were downloaded from GEO with accession number GSE44772. The expression data were quantile normalized. For each module, we created a meta-feature consisting of the average standardized expression of all genes within the module. Linear models including age, sex, postmortem interval, pH, RIN, and the batch was then used to test for association of each module with a diagnosis of Alzheimer’s disease.We replicated the association of the immune modules with Alzheimer’s disease pathology in an independent microarray gene expression data from the cortex (DLPFC) from a previous study by Zhang et al.38. Data were downloaded from https://www.synapse.org/#!Synapse:syn5550404. We TMM normalized the expression data. For each module, we created a meta-feature consisting of the average standardized expression of all genes within the module. Linear models including sex, age, and RIN as covariates were then used to test for association of each module with a diagnosis of Alzheimer’s disease.We replicated the association of the immune modules with Alzheimer’s disease pathology in an independent microarray gene expression data from the cortex (DLPFC) from a previous study by Allen et al.39 in R were used to further disentangle the highly correlated modules and traits. A partial correlation between a trait and a module is the correlation between the trait and module after accounting for the behavior of all other traits and modules. RIN and PMI were included in the partial correlation matrix but removed for visualization. The graphical lasso39 was used to set small partial correlations to zero and was tuned using repeated tenfold cross-validation to estimate the penalty parameter.Partial correlations calculate with the glasso packageWe tested the association of the immune modules with age in an H3K9Ac ChIP-seq dataset generated on the same ROSMAP cohort. For each module, we created a meta-feature consisting of the average peak heights within 10Kb of all genes within the module. Linear models including sex, postmortem interval, RIN, and batch were then used to test for association of module 116 with age.P value (P < 0.05), which then rises above 0.05 when a third variable C is included in the regression, the relationship between A and B is said to be mediated through C. Mediation analysis was performed on the whole cohort and the ROS and MAP cohorts separately.Mediation analysis was performed by performing linear regression and including or excluding the variables displayed in each of the relevant figures. If the coefficient between two variables A and B has a small 40 and GSE9896941. In both datasets, module expression was calculated by calculating the average expression of genes within each module. Only gene homologs that share the same name in human and mouse were included. Both datasets were only explored visually across time and between conditions40 and between cell types41.Data for two mouse studies were downloaded from GEO with accession numbers GSE6439842 was downloaded from https://www.synapse.org/#!Synapse:syn18485175. The authors provided both the raw gene counts and the results from their cell-type clustering. We transformed the counts into cpm, and for each cell calculated the average cpm expression for each of our gene modules. A t-SNE plot of all cells was generated using the average expression of each of the five microglia modules. Four of the authors’ 20 transcriptionally distinct clusters were enriched with microglia markers. We assessed which cluster of microglia cells had the highest m5 expression using a boxplot. For each subject, we calculated the proportion of cells in each of these four clusters relative to the sum of the four clusters. Robust linear regression, using the robust base package in R, was then used to assess the association between the proportions and tau pathology with sex and PMI as covariates.A single nuclei sequencing dataset that was generated from 48 subjects from the ROSMAP cohort43. In short, the system provides a semi-automated approach in which an image is automatically segmented into discrete cells from which summary measures of expression per cell are obtained and used in downstream analyses. For the VASP + validation, TMEM119+ and VASP+ cells were first identified using the default thresholding. Overlap of the positive signals was then considered to identify TMEM119+/VASP+ cells and the lack of co-localization identified the TMEM119+/VASP− cells. The level of spread or ramification was characterized by the compactness parameter, the variance of the radial distance of the object’s pixels from the centroid divided by the surface area of the object. This measure was then compared between TMEM119+/VASP− cells and TMEM119+/VASP+ cells. A random-effects model accounting for between-subject variability was used to assess the significance of differing compactness between the TMEM119+/VASP− cells and TMEM119+/VASP+ cells. A similar analysis was performed for both TRABD and ACADVL.The images were analyzed with the Broad Institute’s Imaging platform, using CellProfiler and CellProfiler Analyst systems to measure and classify cells according to cell morphology44. Participants are non-demented at study entry, agree to brain donation at the time of death, and are evaluated annually with a battery of 21 neuropsychologic tests. A person-specific slope of cognitive decline is calculated for each participant based on 17 tests that are common to the two studies13. The demographic and clinical characteristics of the 540 participants used in these analyses are presented in Supplementary Table ROS and MAP are two large longitudinal studies of aging that were designed and are managed by the same group of investigators so that their data can be merged in joint analyses15 with an average of 95 million paired-end reads of 101 base pairs per participant. To reduce the dimensionality of the RNA-seq data generated from the DLPFC  of each subject, we previously defined modules of co-expressed genes using the Speakeasy algorithm30: there are 47 such modules that contain a minimum of 20 genes and a median of 331 genes. Thus, each module contains a group of genes that have a shared regulatory architecture, and the 47 modules represent the nodes of our network15. The properties of our modules were tested extensively and discussed in our prior manuscript. Module definitions are fairly similar when compared to those derived from other methods, such as WCGNA, to derive groups of co-expressed genes15.The RNA sequence data used in our analyses come from a previously reported dataset17.In this paper, we take a deeper look at the subset of these modules that capture the role of microglia in Alzheimer’s disease and aging. We identified modules enriched for microglial genes as defined in a new reference RNA-seq dataset derived from live, purified human microglia/macrophages extracted from fresh autopsy samples of DLPFC from 10 ROSMAP participants of advanced age: the HuMi_Aged geneset consists of 1,030 microglia enriched genes that were identified based on a fourfold increase in expression in microglia vs. DLPFC expressionP < 0.0011, Bonferroni threshold) for the HuMi_Aged geneset: modules m5, m113, m114, m115, and m116 (Table 32 (Supplementary Table https://www.synapse.org/#!Synapse:syn2580853/wiki/409840). Of the five modules enriched for microglial genes, none are enriched for neuronal genes, but two modules (m113 and m115) are also enriched for astrocyte genes . It contains many well-known myeloid/microglial genes, such as TREM2 and TYROBP. This result is emphasized in a third enrichment analysis of microglial, neuronal, and astrocytic profiles generated from mouse brain34 where we observe that m116 is the most cell-type-specific of all tested modules  correlated in histone 3 lysine 9 acetylation  ChIP-seq data from an overlapping set of 669 ROSMAP individuals, suggesting that the co-expression structure is epigenetically driven15. To obtain an initial perspective on their function, we annotated each module by identifying both known pathways enriched in each module   also has some evidence of enrichment, with well-validated AD genes such as BIN1, being present in m5. These enrichment analyses emphasize the notion that AD susceptibility genes might exert their effect through participating in divergent transcriptional programs of microglia in AD, affecting different aspects of their phenotype and function.We previously reported that m116 is enriched in AD susceptibility genes (P < 0.005), m5 displays a significant association with the burden of Tau (P = 0.0028) and a suggestive association with amyloid (P = 0.0064), while both m113 (P = 0.0043) and m114 (P = 0.00015) are associated with amyloid pathology. These two associations are present in both the ROS and MAP subsets of our discovery dataset . The associations of modules m5 , m114 , and m113  replicate in this repurposed case/control dataset (MSSM1) derived from the same brain region in brain bank samples. Further, we repurposed an RNA-seq dataset from the temporal cortex and the cerebellum of subjects in different diagnostic groups selected from the Mayo Clinic brain bank38. Here again, we imposed our module definitions on this third dataset, and we find that m5, m113, and m114 are upregulated in the temporal cortex but not the cerebellum of subjects classified as AD relative to control subjects  and m114 , but not the other three modules. We observe that women have higher levels of both m5 and m114, which is replicated in the repurposed MSSM1 case/control dataset with m5  and m114 . To assess whether changes in the expression level of these modules may contribute to the effect of sex on AD and AD endophenotypes, we included the module information in a regression model and found that m114 explains 39% of the variance in amyloid pathology that is explained by sex, and m5 explains 13% of the variance in Tau pathology that is explained by sex . This result suggests that either the number or proportion of microglia in the neocortex (best captured by m116) is higher with age or that the expression of this set of genes is higher with age. The latter of these two hypotheses seems to be most likely as this m116:age association did not replicate in the H3K9Ac ChIP-seq epigenomic data  or in an evaluation of microglia counts based on histology in relation to m11610 , despite its strong effect size in the RNA data. These results suggest that (1) the effect of age is not explained by alterations in chromatin structure, and (2) there is no significant change in the proportion of microglia in the tissue. Thus greater RNA transcription of m116 genes from the same number of microglial cells is the most likely explanation for the association with age. This is consistent with our report that the total number of microglia derived from histological data is not increased in AD29. While m116 is not directly related to the pathology measures or to cognitive decline, the overexpression of these genes in microglia of older individuals could contribute to the mechanisms behind aging, which is the single greatest risk factor for AD.Advancing age is only associated with higher expression of m116   differences that exist between mouse and human microglia in the context of aging, proteinopathy models, and human AD pathology.While we appreciate that murine models that mimic aspects of amyloid and tau pathology have limited relevance to human disease10: the proportion of microglia with an activated stage III morphology based on immunohistochemical studies  experimentVASP, that (1) had antibodies available for immunofluorescence studies and (2) statistically captured the effect of m5 on tau pathology and cognitive decline. At the single gene level, VASP RNA expression in cortical tissue is also strongly associated with activated microglial counts and cognitive decline . Our modeling suggests that VASP should be expressed in microglia and should be expressed at a higher level in microglia that have a morphology consistent with activation according to standard neuropathologic assessment .To take these analytic results to the next stage and confirm our results in situ, we selected a representative m5 gene, ne Table , as wellVASP may be a representative gene for immune module m5, it has not been explicitly reported to be expressed in microglia previously. We first demonstrated that VASP is expressed in cells labeled with TMEM119, a protein proposed as a pan-microglial marker not expressed on infiltrating macrophages49  the level of VASP expression in each cell and (2) several different morphologic features of each cell. Surveying the 4158 TMEM119+ cells captured from cortical tissue sections of six subjects obtained from Rush Alzheimer’s Disease Center, we find that TMEM119 + VASP + microglia are less ramified than TMEM119 + VASP− cells    Fig. . This as17) Fig. , a measu17) Fig. . To furtWe performed a detailed in silico dissection of five cortical transcriptional modules enriched for genes found in aging human microglia and differentiated the role of m116 as a module reflecting microglial aging from those of m113 and m114 that may be involved in promoting the accumulation of β-amyloid and of m5 that captures a morphologically activated microglial state that may contribute to the accumulation of Tau. Thus, we begin to empirically differentiate groups of microglial genes that work together to accomplish specific, distinct functions. This regulatory architecture is present at the chromatin level: our H3K9Ac data both confirm the correlation of genes within RNA-seq-defined modules and validates their association with AD-related traits. It is also reflected by largely-non-overlapping patterns of transcription factor binding site enrichment for each module  are all present in the brain of aged individuals. This observation highlights the complexity that we face in developing immunomodulatory therapies for AD: they will have to be tuned to specifically engage or shut down a given transcriptional module while not exacerbating the ongoing perturbation of others. In the case of Tau, we propose the hypothesis that increased expression of m5 by microglia contributes to the accumulation of Tau aggregates; this is the most likely scenario based on our cross-sectional data and robust methods for modeling mediation statistically. However, we cannot determine formal causality with cross-sectional data, and longitudinal studies using appropriate combinations of PET tracers or other biomarkers for Tau and microglial function are needed to validate this putative causal chain. We note that this hypothesis is consistent with the results of our modeling of histological measures of microglial activation29 which proposes that morphologically activated microglia contribute to the accumulation of Tau pathology. Thus, transcriptional and histological data converge to suggest that increased prevalence of activated microglia leads to more Tau accumulation, suggesting that downregulation of m5 may be a therapeutic option. The relatively specific enrichment of the JUN kinase pathway in m5 suggests that it may be regulated in a manner that is somewhat distinct from the other four modules and could provide a relatively specific target for drug development.Importantly, given our large sample size, we can identify different modules involved in β-amyloid and Tau pathology. Our analyses suggest the existence of multiple microglial functional modules active in parallel within the same brain: these transcriptional programs are working in parallel. In the case of m5, we pushed validation efforts further to confirm, in situ, the hypothesis that it reflects the presence of a subset of microglia that are more activated and found in greater numbers as Tau accumulation increases in the tissue, a result consistent with immunohistochemistry-based observations on this cohort16, and our m116 results suggest that microglia may be involved in AD through mechanisms that remain elusive today. We clearly demonstrate the presence of an effect of aging on microglia, but this effect does not translate directly into pathology in our analysis. One thing to appreciate is that many AD GWAS often use samples of convenience as control subjects: these subjects are only coarsely characterized. Further, since AD incidence increases with advancing age, the majority of control subjects are likely to develop AD if they live long enough. Thus, the AD GWAS may have uncovered genes that have a strong effect on advancing the age of onset of AD and emerge as susceptibility genes because of the study design. Such an effect would tie in with our results where TREM2, INPP5D, and other genes found in m116 are associated primarily with microglial aging7.A surprising result is the lack of strong association between m116 and β-amyloid or Tau pathology: it is contrary to a simple narrative that this module, enriched for AD susceptibility genes from GWAS, exerts its effect primarily through the increased accumulation of the neuropathologies that define AD. β-amyloid and Tau burden only explain some of the variance of cognitive decline and AD dementia50. Nonetheless, until now the molecular identity of the plaque-associated (ameboid or stage III) activated microglia was obscure. This study takes the first steps to assign, though in an indirect way, a transcriptomic signature to the morphologically defined stage III subpopulation of microglia. VASP, TRABD, and ACADVL may be surrogate markers for stage III microglia.One possibility is that the different transcriptional modules described here correspond to different microglia subpopulations. Given their sentinel and effector nature, the diversification of microglia phenotypes in specialized niches in the aged brain parenchyma burdened with β-amyloid and Tau deposits is a possible scenario. A longstanding observation is the morphological transformation of parenchymal microglial cells in and around amyloid plaques in ADCD33). Further, these microglial modules are likely to contain genes expressed in peripheral monocytes. However, given that monocytes represent such a small minority of the myeloid cells found in the brain, their effect on the cortical transcriptome is likely completely diluted and unlikely to explain the associations that we have uncovered. Second generation maps derived from isolated microglia and transcriptional profiles of single microglia will be essential to better understand the involvement of microglia in the pathophysiology of AD and the complex architecture of microglial subsets in the aged and AD brain.Nonetheless, our study has certain limitations; foremost amongst these is the fact that our pathologic measures are cross-sectional since they are obtained at autopsy. We therefore cannot comment formally on causality or on the exact sequence of events that is occurring in the living human brain. Our analysis of mouse data from the TAU, TAS, and TPM models suggest that their utility in dissecting human microglial responses to Tau and amyloid aggregation may be limited: they do not recapitulate the observations that we report in the ROSMAP samples and validate in two independent collections of human samples. Further, this neuroimmune network map should be seen as a first draft: microglia represent a minority of the cells found in the human cortex and have a relatively low quantity of mRNA in their cytoplasm. Thus, the level of expression of microglial genes, particularly those present only in a subset of microglia is likely to be underestimated or even absent from the tissue level data (e.g., CD33, TREM1, and TREM2 are contributing to regulate the level of activation9, and, as seen in other immune functional programs, small differences in the level of receptor engagement or the presence of co-stimulatory molecules can result in dramatically different responses that can sometimes be the opposite of an anticipated response. By refining the set of microglial genes implicated in β-amyloid vs. Tau pathology, we mark a step forward in better targeting drug development programs both by proposing new targets and by defining novel outcome measures that can be used to assess the functional consequences of lead compounds.Overall, we have generated an initial framework on which we and others can assemble additional data from in vivo and in vitro experimental models: when perturbing a particular gene of interest, we have to be cognizant of its membership to a given module of co-expressed genes and to the effect of such perturbations on different transcriptional programs found in microglia that may not be directly measured in an experimental system. The immune system is exquisitely modulated by a complex set of checks and balances in which AD genes such as Supplementary materialSupplementary table 4Supplementary table 5Supplementary table 6Supplementary table 8


Drosophila is a well-established paradigm to study transcription in developmental biology. The rapid temporal dynamics of gene expression during early stages of development, however, are difficult to track with standard techniques. We optimized the bright and fast-maturing fluorescent protein mNeonGreen as a real-time, quantitative reporter of enhancer expression. We derive enhancer activity from the reporter fluorescence dynamics with high spatial and temporal resolution, using a robust reconstruction algorithm. By comparing our results with data obtained with the established MS2-MCP system, we demonstrate the higher detection sensitivity of our reporter. We used the reporter to quantify the activity of variants of a simple synthetic enhancer, and observe increased activity upon reduction of enhancer–promoter distance or addition of binding sites for the pioneer transcription factor Zelda. Our reporter system constitutes a powerful tool to study spatio-temporal gene expression dynamics in live embryos.The gene regulatory network governing anterior–posterior axis formation in  Drosophila embryos. They also find that reduced enhancer–promoter distance or addition of binding sites for the pioneer transcription factor Zelda increases enhancer activity.Ceolin et al. optimise the fluorescent protein mNeonGreen to generate a reporter to measure enhancer expression in living  A striking example is the organization of the anterior-posterior body axis in the early stages of Drosophila melanogaster (D. melanogaster) embryonic development. Here, a hierarchically organized network of transcription factors (TF) rapidly subdivides the embryo into progressively smaller regions of expression, which prefigure the segmental body plan of the larva4. The process is mediated by a large number of transcriptional enhancers, which typically receive input from multiple TF to drive the expression of single elements or portions of complex downstream gene expression patterns with stunning precision5. This so-called segmentation network has long served as a key paradigm to decipher how transcriptional control is mediated by enhancers7. While much progress has been made, culminating in computational models that predict the expression of segmentation enhancers from their sequence with reasonable accuracy9, we are still far from an exact quantitative understanding of how expression is “computed” through the combinatorial occupancy of TF molecules at often long stretches of enhancer DNA, and their interplay with nucleosomes and other features of chromatin organization. To achieve further progress, it will be vital to systematically investigate the effects of modulations of all relevant features of enhancer sequences, such as number, strength, and arrangement of TF binding sites in either mutated native or synthetic enhancer sequences10. This in turn requires methods to efficiently quantify the transcriptional activity induced by enhancers at high spatial and temporal resolution, and with medium to high throughput.The precise spatio-temporal control of gene expression by transcriptional enhancers is key to a variety of biological processes, from animal development to cancer biology11. However, as sensitive as these labeling methods can be, they all rely on the staining of fixed embryos. Thus, the measurement of expression at multiple time points is labor-intensive and subject to difficulties in precise staging, which limits their usefulness in studying transcriptional dynamics. Using live markers is the obvious solution, and fluorescent protein reporters such as GFP have been used extensively to study gene expression dynamics. However, their application in early D. melanogaster embryos has been hampered by the fact that the maturation time of most fluorescent proteins is slow relative to the very fast dynamics of gene/enhancer expression, leading to delayed and unfaithful representations of patterns12. To overcome this problem, researchers have turned to in vivo mRNA labeling. The MS2-MCP system13 is a technique that directly captures the temporal dynamics of an enhancer’s activity in living cells and organisms. It is based on the coexpression of two components: an MCP-GFP fusion protein, which is provided maternally, and a series of multiple RNA hairpins (MS2 loops) integrated into the sequence of the transgene of interest. As they form, the hairpins are recognized and bound by MCP-GFP; the resulting local accumulation of MCP-GFP molecules is detected as fluorescent spots over a background of the unbound MCP-GFP. This inherent fluorescence background limits the sensitivity of detection. While in cell culture single-molecule sensitivity can be reached, the detection threshold in D. melanogaster embryos is much higher, due to more difficult imaging conditions . Despite this limitation14, the MS2-MCP system has allowed to unravel new aspects of enhancer activity15 in D. melanogaster embryos, such as the role of enhancers in controlling transcriptional bursting16.Over the years, techniques to measure transcriptional activity have steadily improved. Historically, in situ hybridization in fixed embryos has been the method of choice for the determination of mRNA expression patterns. More recently, single molecule fluorescent in situ hybridization has reached single molecule sensitivity, measuring both cytoplasmic and nascent mRNA transcripts17, coupled to nuclear localization signals (NLSs) and sequences boosting translational efficiency. Enhancer-driven expression of the reporter is monitored by confocal fluorescence microscopy. Since protein concentration is only an indirect readout of transcription, we reconstruct mRNA levels by analyzing the time course of mNeonGreen fluorescence intensity with a model of ordinary differential equations (ODEs). We validate our approach by comparing our data with those obtained with the MS2-MCP system for the well-studied hunchback anterior (hb_ant) enhancer8, and by measuring the activity of the Kr_CD2 enhancer. In addition, we challenge the sensitivity of our reporter system by measuring the weak expression pattern driven by a short synthetic enhancer carrying three binding sites for the maternal activator Bicoid (Bcd). Finally, we use our reporter to quantify how structural or compositional changes affect the activity of this simple enhancer. We measure quantitative differences in the activity of two variants, one where the enhancer-promoter distance is reduced and another in which three binding sites for Zelda (Zld), thought to promote an open chromatin structure18, are added. Our data show that the distance between an enhancer and its target promoter affects expression strength, and that Zld influences both the intensity and dynamics of Bcd-dependent transcription.Here, we present a reporter system for tracking enhancer expression that aims to be both more sensitive and better suited to large scale investigations of enhancer function than the MS2-MCP system. Our approach is based on expression of an optimized reporter protein that consists of the bright and fast maturing fluorescent protein mNeonGreenD. melanogaster embryos requires a quantitative, sensitive, and scalable method. We reasoned that a good starting point might be found among the faster-maturing fluorescent proteins that have been developed in recent years. We considered various fast-maturing fluorescent proteins and settled on the recently discovered fluorescent protein mNeonGreen17, which has the highest known ratio of molecular brightness over maturation time19 and has already been used successfully as a fluorescent tag and expression reporter20. To optimize the reporter for our purposes, we added a number of modifications: In order to minimize the diffusion of the reporter protein away far from the region where it is synthesized, which can occur due to the syncytial nature of the blastoderm embryo, we added multiple NLSs at both the C- and N-terminus 23. We refer to the full sequence of the reporter construct including 3′UTR and 5′UTR as mNeonRep.Tracking the rapid spatiotemporal dynamics of enhancer activity during the early development of nus Fig. . The enrnce Fig. , which h24. We modeled expression and maturation of the mNeonGreen reporter with a set of linear ODE  that we typically observe with our reporter. In addition, we tested the ability of the algorithm to discriminate between different dynamics of enhancer activity, and in particular, to detect when an enhancer is turning on or off ; the excitation laser power was optimized to obtain maximal fluorescence signal while minimizing photodegradation . We observed the typical MS2-MCP fluorescent spots in the anterior half of the embryo  or reconstructed (mNeonRep) are very similar  ), expressed under the control of the lar Fig. , with a 95) Fig. .Fig. 2Th25. Previous studies used in situ hybridization to characterize the expression of this enhancer, termed Bcd3, and observed a weak signal in the anterior part of the embryos26. Using mNeonRep, we detect a weak but reproducible , the second the enhancer accessibility . To change enhancer-TSS distance, we removed a linker sequence between the Bcd3 enhancer and the DSCP promoter, thereby shortening the distance between the TSS and the most proximal Bcd binding site from 146 bp to 73 bp . The comparison of the average expression profiles shows that the activity of the Bcd3-proximal enhancer is indeed consistently ~2.5-fold stronger than that of Bcd3  compared to the Bcd3 enhancer , which in turn would lower the time resolution with which transcriptional dynamics can be resolved. Other approaches that have successfully been used in cell culture to increase mRNA detection sensitivity, like fine-tuning MCP-GFP expression or using MCP-GFP dimersDrosophila blastoderm the spatial resolution is limited by the fact that the embryo at this stage is a syncytium, and in the absence of cellular membranes both the reporter mRNA and the protein can diffuse away from the site of production. However, this process is likely limited because of the NLSs engineered into the reporter, and the resulting strong enrichment of the fluorescence signal in the nuclei. Moreover, a recent study showed that diffusion of mRNAs between neighboring nuclei in the D. melanogaster blastoderm is limited to only 1–2 nuclei33. Indeed, we find that the expression patterns obtained with our reporter, and in particular those for the narrow Kr_CD2 central stripe, are highly similar to those measured with other techniques; this would not be the case in the presence of rapid diffusion of either reporter mRNA or protein.The advantages of the mNeonRep reporter system come with modest losses in spatio-temporal resolution. While our reporter permits single-cell resolution in principle , in the Drosophila blastoderm with high fidelity. The achievable temporal resolution depends on the SNR of the protein fluorescence. Based on numerical simulations, we estimate that under our imaging conditions the reconstruction algorithm is able to resolve rapid changes in enhancer activity  with a resolution of about 7 min  revealed a non-linear dependency, with greatest activity at a distance of around 70 bp30. Our findings confirm such a dependency, which may reflect the probability of contacts between enhancer and core promoter, for Drosophila. Note that changing the enhancer-promoter distance tunes the level of expression without altering dynamics: both Bcd3 and Bcd3-proximal show a rapid reduction of transcription early in nc14, and throughout the time course and at all positions, the Bcd3-proximal enhancer behaves simply like a rescaled version of the Bcd3 enhancer.To further validate and illustrate the performance of our mNeonRep reporter, we used it to investigate the differences between three variants of a simple synthetic enhancer, consisting of binding sites for the maternal activator Bcd. Although near identical in their spatial expression pattern, the three enhancers show clear differences both in their absolute levels of activity and in their temporal dynamics, thus illustrating the ability of the reporter to capture subtle quantitative differences as well as their development over time. They also provide interesting biological insight. First, the 37 to the Bcd3 enhancer not only dramatically boosts activity levels, but also changes the dynamics of expression: Unlike Bcd3 and Bcd3-proximal, the activity of Zld3-Bcd3 remains sustained throughout nc14, with steadily increasing cumulative levels of mRNA, and an increasing activity ratio relative to Bcd3 at all positions. Binding of Zld to its cognate sites promotes the deposition of histone modifications and increases DNA accessibility38, and inserting binding sites for Zld in native or synthetic enhancers has been reported to increase enhancer activity and accelerate enhancer activation after nuclear divisions31. More specifically, single molecule studies have shown that Zld creates local hubs of increased Bcd concentration in the vicinity of enhancers, thus increasing site occupancy39. The increase in absolute activity resulting from the insertion of Zld sites into our Bcd3 enhancer is therefore expected. The change in expression dynamics is more intriguing: Since the decrease in Bcd-dependent transcription early in nc14 is thought to result from the sumoylation of the Bcd protein41, the sustained activity in the presence of Zld sites could result from a threshold effect, with greater enhancer accessibility boosting the ability of low levels of remaining Bcd to activate transcription, or it could be the result of a more direct interference of Zld with Bcd deactivation, for example by sequestering Bcd in a local microenvironment at the enhancer where sumoylating enzymes are not present.By contrast, adding three binding sites for the pioneer transcription factor ZldIn summary, we have shown that our mNeonGreen reporter system is a powerful tool to study transcriptional dynamics, and is particularly suited for studies that aim to quantify expression dynamics of large numbers of native or synthetic enhancers, including constructs with weak expression levels. Compared to existing methods, our approach offers important advantages in terms of sensitivity and throughput, and it can, with suitable adaptations, be applied in other organisms or cell cultures.Eurofins genomics GENEius software package - Munich, Germany) mNeonGreen17 coding sequence, obtained by gene synthesis, to three different NLSs: the Bipartite-N-term NLS42, the SV40 NLS, and a Class3 C-term NLS21. All enhancers were coupled to a strong synthetic core promoter (DSCP)43. The sequence of hb_ant enhancer was amplified from genomic DNA. The sequence of the Bcd3 enhancer25 was generated by oligo annealing. The sequence containing 3 Zld binding sites was designed by inserting three consensus binding sites for Zld44 into a neutral background described in previous works10, and was generated by oligo annealing. In all constructs, except for Bcd3-proximal, a 73-bp linker separated the enhancer from the basal promoter. This sequence does not contain any predicted binding site for TF of the segmentation network, based on available position weight matrices (PWM)44. To optimize translation of the construct, we included the IVS + Syn21 translational enhancer sequences22 at the 5’UTR and the p10 terminator sequence22 at the 3'UTR. We refer to the full sequence of the reporter including 3'UTR and 5'UTR as mNeonRep. All elements were cloned into an expression construct based on the pBDP backbone  as described in45 with only one difference: the insertion of an additional 340 bp long neutral spacer46 upstream of the enhancer. This was done in order to minimize the potential interference by binding sites for segmentation TFs in the backbone of the reporter plasmid, which were revealed by a preliminary analysis.The mNeonGreen reporter construct was generated by C- and N-terminal fusion of a codon optimized  was fused upstream of the yellow reporter gene coding sequence . The 24xMS2-yellow sequence was then cloned immediately downstream of the enhancer-linker-DSCP sequence into the same pBDP backbone described above for the mNeonGreen reporter construct. A complete list of all sequences is provided in Supplementary Information.43. Homozygous fly stocks were generated by crossing a single male with a single homozygous virgin female, and the insertion of the correct construct was verified by single-fly PCR of both parents and sequencing of the PCR products.All reporter plasmids for both the mNeonGreen and MS2 reporters were integrated in the same attP2 docking site using PhiC31 integrasemNeonGreen: Enhancer-mNeonRep embryos were collected, dechorionated in 50% bleach and mounted between a semipermeable membrane and a microscope cover glass, immersed in halocarbon oil (Sigma). Imaging was performed at 24 ± 1 °C on a Zeiss LSM710 confocal microscope using a 40×1.2NA water immersion objective. The laser excitation power was chosen to provide maximal fluorescence intensity while keeping photodegradation negligible: with our setup, we found that the optimal excitation, measured at the entrance pupil of the objective was 8 µW  virgins were crossed with males carrying either the hb_ant-DSCP-MS2-Yellow or Bcd3-DSCP-MS2-Yellow reporter genes. Embryos were collected and mounted as described above. Imaging was performed on a Zeiss LSM710 confocal microscope using a 63 × 1.4NA oil immersion objective. Pixel size was set to 0.33 µm and the image field of view to 169 µm x169 µm. A stack of 15 images separated by 1.3 µm in z was acquired at each time point. The final time resolution was 60 s per z-stack. At the end of each movie, a single tiled z-stack with a much larger field of view of 500 µm × 840 µm was acquired to precisely locate the imaged area within the whole embryo.47. To measure the maturation rate of fluorescence of the reporter protein in the embryo, translation was blocked by injecting cycloheximide (Sigma-Aldrich: 01810) using a needle, at the concentration of 0.9 mg/ml48. In both cases, Enhancer-mNeonRep embryos were collected, dechorionated in 50% bleach, glued on a coverslip and dried at 18 °C for 20 min prior to the injections. Injected embryos were imaged as described above; for each condition, three replicates were measured, with nearly identical results.To measure the degradation rate of reporter RNA in the embryo, transcription was blocked by injecting alpha-amanitin (Sigma-Aldrich: A2263) using a needle, at the concentration of 0.4 mg/mlDefiniens XD 2.0 software package . In brief, the contour of the embryo was first identified using the strong autofluorescence signal arising from their vitelline membrane. The cortical region containing the nuclei was then defined by shrinking the contour line of the embryo twice by a few pixels toward its interior  to be the moment at which nuclear membranes reappear throughout the embryo after mitotic division following NC13, and used DIC images to detect this. Time zero was determined with a precision of ±1 min, which also matched the time resolution of our measurements.In order to compare expression patterns between different embryos it is important to precisely register them in time relative to a common reference point. We chose this time point , the concentration of nonmature dark reporter protein D(t), and the concentration of the mature fluorescent protein F(t). Maturation of the protein and degradation of both protein and mRNA is accounted for by the rates protein maturation kmat, protein degradation kdp, and mRNA degradation kdm. Using the linearity of this model and discretizing the time, this system of differential equations can be rewritten as a linear model. The linear model links, through a model matrix H, the array of observed fluorescence F(ti) to the mRNA production rate mp(tj) and to the initial concentrations of mRNA m(0), immature and mature protein P(t0) and F(t0).The model includes the concentration of reporter mRNA kdm, protein degradation kdp, and protein maturation kmat.The matrix H depends only on the structure of the original ODE system and on the rates of mRNA degradation F(ti) using a regularized non-negative least square algorithm which determines the rate of RNA production minimizing:The linear model can be fit to the time course of protein fluorescence under the assumptions that the mRNA production rate and all concentrations must be positive definite. After fitting we calculated the cumulative mRNA production as:In order to estimate how sensitive to the noise the reconstruction is, we implemented a bootstrapping of residuals algorithm. We first checked that the residuals of the fit showed no correlation at different times , then applied a second 3D-Gaussian filter, again with a kernel size of 5 × 5 × 3 pixels, (GaussianFilter2). We then subtracted GaussianFilter2 from Gaussianfilter1, which resulted in a background subtracted image. As a last step, we applied a global threshold and carried out segmentation using a so-called Multi-Threshold Segmentation algorithm implemented in the Definiens software platform. Briefly, this algorithm splits the image domain and classifies the resulting image objects based on a defined pixel value threshold. To reject particles resulting from segmentation errors, we filtered the resulting image objects by setting a threshold based on particle volume. Hence, with this procedure we avoid to include in our data artefacts due to detector shot noise, fluctuations of GFP background, or embryo autofluorescence.MS2 fluorescent spots were detected in 3D with 13 we calculated the MS2 signal as the integral of the fluorescence of each particle minus the local GFP background that was estimated from a spherical shell surrounding each detected spot. To compensate for differences in imaging depth, the signal was further normalized by the average local background.Following a previous studyTo calculate the expression profiles, the data were binned based on their AP position, by using 2% AP bins. We defined the total mRNA production rate in each bin as the sum of MS2 signal in the bin. This allowed for integrating both the information on the fraction of active nuclei and on the intensity of transcription in each cell. Since the imaged portion of embryo at different AP position is not uniform (Supplementary Fig. To assess the reproducibility of the measurements performed with the mNeon reporter, we measured three biological replicates. Each replicate is an independent measurement of a different embryo of the same stock. The confidence intervals on the reconstructed mRNA level of each replicate have been calculated by bootstrapping, details about the bootstrapping procedure are provided in “Methods” section: “Analysis of Reporter time course and mRNA reconstruction.” Where data from a single replicate are shown with a confidence interval, the interval represents the first and third quartile.Further information on research design is available in the Nature Research Reporting Summary linked to this article.Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Movie 1Supplementary Movie 2Supplementary Movie 3Supplementary Movie 4Supplementary Movie 5Reporting Summary