In addition to increased aberrant protein aggregation, inflammation has been proposed as a key element in the pathogenesis and progression of Alzheimer’s disease. How inflammation interacts with other disease pathways and how protein aggregation increases during disease are not clear. We used single-molecule imaging approaches and membrane permeabilization assays to determine the effect of chronic exposure to tumour necrosis factor, a master proinflammatory cytokine, on protein aggregation in human-induced pluripotent stem cell-derived neurons harbouring monogenic Alzheimer’s disease mutations. We report that exposure of Alzheimer’s disease neurons, but not control neurons, to tumour necrosis factor induces substantial production of extracellular protein aggregates. Aggregates from Alzheimer’s disease neurons are composed of amyloid-β and α-synuclein and induce significant permeabilization of lipid membranes in an assay of pathogenicity. These findings provide support for a causal relationship between two crucial processes in Alzheimer’s disease pathogenesis and suggest that targeting inflammation, particularly tumour necrosis factor, may have beneficial downstream effects on ameliorating aberrant protein aggregation and accumulation. There is a complex interplay between neuroinflammation and protein aggregation in Alzheimer’s disease. We show that chronic exposure to the inflammatory cytokine tumour necrosis factor stimulates monogenic Alzheimer’s disease neurons to produce significantly more toxic protein aggregates, with no effect on healthy neurons, indicating a causal relationship between neuroinflammation and protein aggregation.  Accumulation of misfolded proteins produced by neurons, initially extracellular deposits of amyloid-β (Aβ), followed by intracellular aggregation of tau are thought to be primary initiating mechanisms in Alzheimer’s disease pathogenesis . DownstrSeveral lines of evidence support the hypothesis that inflammation, either from the disease itself, or derived from secondary insults, augments pathology and cognitive decline in Alzheimer’s disease. First, increased levels of central and systemic proinflammatory cytokines are observed in mild cognitive impairment and are positively correlated with progression into severe Alzheimer’s disease (in vitro from induced pluripotent stem cells (iPSCs) derived from individuals carrying disease-causing mutations in amyloid precursor protein (APP) or presenilin 1 (PSEN1) display robust and reproducible alterations in extracellular Aβ species , a key mediator common to both central and systemic inflammatory pathways, and measured changes in the production and composition of extracellular aggregates of disease-associated proteins. Using ultrasensitive single-aggregate imaging techniques  or the vehicle control were started 60 days post-neural induction and continued for a duration of 18 days. During this period, a complete exchange of cell culture media was performed every 48 h. The secretomes of neuronal cultures were collected every 6 days, centrifuged at 800 RCF for 3 min to remove cellular debris and stored at −20°C until required for analysis. For washout experiments, cultures were switched from TNF treatment to the vehicle control after the collection of secretomes at Day 12.Quantification of Aβ38, Aβ40 and Aβ42 was performed with the V-PLEX Aβ peptide Panel 1 kit (K15200E) and a Quickplex SQ120 instrument  using 25 µl of cell culture supernatant collected at Day 18 of TNF treatment.CLTC, GAPDH, GUSB, PPIA, RPLP1, RPS15A and RPS9).To confirm the cortical identity of iPSC-derived neuronal cultures used in every experiment, Trizol-extracted RNA from Day 18 vehicle-treated cultures of each genotype was profiled using a custom gene expression panel on the Nanostring platform (Nanostring Technologies). Mean negative control probe counts were subtracted from sample gene counts, before normalization using the geometric mean of six positive control probes and seven housekeeping genes  and dsRed (ex561/em570–630) laser and filter sets. For image processing, the number of red nuclei (indicating dead cells) was divided by the number of blue nuclei  within each field of view to obtain a measure of the proportion of dead nuclei per condition.Live imaging of cell viability during the course of TNF treatment was performed by incubating the cultures with NUCLEAR-ID Blue/Red cell viability reagent (Enzo) diluted in culture media for 30 min, according to the manufacturer’s instructions. Cultures were washed in fresh media before images were acquired on an Opera Phenix imaging platform (Perkin Elmer) at 37°C and 5% COImaging of the aggregates was performed using a purpose-built total internal reflection fluorescence microscope. The intensities of 405 nm , 488 nm (TOPTICA Photonics) and 561 nm  lasers were first attenuated using neutral density filters, circularly polarized using quarter wave plates, expanded using telescopes and then passed through appropriate filters . The beams were then made concentric using dichroic mirrors  and directed into the back port of an inverted Ti-E Eclipse microscope (Nikon). The light was passed through a 1.49 N.A., 60× total internal reflection fluorescence objective. Fluorescence was collected by the same objective, separated from excitation light using dichroic mirror  and passed through filters appropriate for the fluorophore . The fluorescence was then expanded using a 2.5× relay lens and focussed onto an electron-multiplying charge-coupled device for imaging.Aptamer DNA points accumulation for imaging in nanoscale topography (ADPAINT) and single-aggregate visualization by enhancement (SAVE) imaging was performed as described previously  . Preliminary experiments found that extracellular aggregates were clearly detected after 10–12 days, and robustly increased after 16–18 days of TNF treatment, therefore, in the experiments reported here, cell culture media were replaced every 2 days and collected every 6 days for analysis . These cor TNFR2 .APP promoter  and Intron 4 (8–9-fold low and 13–14-fold increase high) neurons using both imaging techniques  (PSEN1 M146I (reduction of 14.2% ± 6.7%) (PSEN1 Intron 4 neurons (reduction of 32.7% ± 0.9%) (PSEN1 mutant neurons that have the capacity to permeabilize lipid membranes.Pre-incubation with an Aβ-specific nanobody (Nb3) ( ± 1.9%)  and Intr ± 1.9%) . Pre-inc ± 6.7%)  and PSEN ± 0.9%)  but by aPSEN1 mutant neurons, but not healthy control neurons, results in increased release of toxic extracellular protein aggregates. Using single-molecule imaging techniques and membrane permeabilization assays, we determined that these aggregates contain Aβ and/or α-synuclein, potentially with other proteins, and have the capacity to permeabilize lipid membranes. We previously observed an increase in the ability of aggregates in CSF to permeabilize membranes with no accompanying change in aggregate number when comparing patients who are mildly cognitively impaired and hence at the early stages of developing Alzheimer’s disease compared to controls , have previously been shown to cause an inflammatory response via TLR4, leading to production of proinflammatory cytokines including TNF , which iThe amyloid cascade hypothesis, which proposes that aggregation of Aβ is the central pathological event in Alzheimer’s disease pathogenesis , has dirHaving established that TNF promotes protein aggregate production from monogenic Alzheimer’s disease neurons, which both generate longer, hydrophobic forms of Aβ and have dysfunction of their endolysosomal-autophagy network, a wide range of follow-up studies will be of interest. This includes exploring the cellular mechanisms underlying this process, the effect of other Alzheimer’s disease-associated mutations and if similar changes can be observed in the control neuron, if exposed to TNF or a combination of proinflammatory cytokines for longer times. It will also be of interest to investigate the potential proinflammatory properties of the secreted aggregates and their capability to cause LTP deficit in rodent models.in vivo, with early stage inflammation, either from the disease itself, or secondary insults, leading to TNF release and subsequent acceleration of the release of protein aggregates, which in turn drives further disease progression. Consequently, directly targeting these aggregates and reducing the levels of TNF and other inflammatory cytokines are both potential therapeutic targets for slowing Alzheimer’s disease pathogenesis and may be more effective when used in combination. Finally, studies of iPSC-derived neurons in the presence of chronic levels of TNF may be a more realistic model of Alzheimer’s disease than studying neurons in the absence of any cellular stress, since the secreted aggregates have similar size distributions to Alzheimer’s disease CSF and show an increase in number as observed in Alzheimer’s disease.Overall, our results point to complex interactions during Alzheimer’s disease initiation Brain Communications online.fcaa146_Supplementary_DataClick here for additional data file.


The lipid analysis of commercial lamb's meat, from two main Moroccan production areas MiddleAtlas and highlands of eastern Morocco, was conducted. This study concerns the lipid qualityof sheep meat from Beni Guil (BG) and Timahdite (Ti) as indigenous breeds and that of theOuled Djellal (ODj) breed of Algerian origin. To study the effect of the geographical area, themeat samples from the Beni Guil breed were taken in the two main locations of this breed: in theregion of Ain Beni Mathar (BGA) and that of Tendrara (BGT). The fatty acid profiles of the sheepmeats analyzed showed the following: (i) polyunsaturated fatty acid (PUFA) richness was of 12.87 % and20.59 % respectively for BGA and BGT breeds; (ii) polyunsaturated  Cardiovascular diseases (CVDs) and cancer are among the mostimportant causes of human mortality and are strongly related to a diet of cholesterol andsaturated fatty acids . However, the intramuscular fat of meat is a caloricallydense macronutrient which plays a key role in human nutrition due to its richness in essential fattyacids . Several studies showed that red meat from pasture has lowerintramuscular fat and balanced ratios of lipid quality than animals from feedlots . Indeed, recent studies showed that certain fatty acids (FAs) in thesemeats can have a very favorable effect on human health, such as rumenic acid . Human nutritionists, public health institutes, and various authorities are recommendingbalanced ratios between saturated fatty acids (SFAs) and unsaturated fatty acids (UFAs), especially ofpolyunsaturated FAs (PUFAs) In Morocco, 95 % of the sheep population is composed of local breeds , and its production is mainly based on pastures . The trend towards the lean meat consumption from pastures places the sheep meatsof indigenous breeds in a favorable position compared to those of selected or imported breeds for abetter production (breeds that have high productivity). This study concerns the evaluation of lipidquality of sheep meat from Beni Guil (BG) and Timahdite (Ti) as indigenous breeds and that of theOuled Djellal (ODj) breed of Algerian origin, currently competing with BG in eastern Moroccohighlands due to its weight characteristics.22.1Sheep livestock in Morocco is generally destined for meat production where the majority of the malesare intended for the feast of the sacrifice, and about 25 % of female lambs are reserved toreplace the elderly ewes. The rest are fattened and intended for slaughter to supply the red meatmarkets throughout the year. In this study, 48 carcasses of female lambs fromthree sheep breeds were reared in Moroccan pastures. Two indigenous breeds . They adopt arhythm of lambing per year. Sampling was done with the assistance of the slaughterhouseveterinarian. Forty-eight longissimus lumborum muscles (LLM) were obtained 24 In the pastoral system adopted by breeders of eastern Morocco and the Middle Atlas, the adopted feedingcalendar is dominated by grazing that lasts up to 8–12 Stipa tenacissima) and wormwood steppes. In July–August, the animals feed on cereal stubbles. The dominant plant species in thesteppe and highland pasture of eastern Morocco were wormwood , halfahgrass (Stipa tenacissima), esparto grass (Lygeum spartum), Atriplex halimus, ray grass (Lolium perenne L.), laser white (Laserpitium latifolium),and sweet broom (Arthrophytum scoparium), with the presence of other species, such asBromus spp. Eruca vesicaria (roquette), Stipa capensis, andMedicago spp. .In the eastern region of Morocco, animals move south in winter and north in summer in the same areato graze on the halfah grass  Quercus rotundifolia and Q. ilex, Stipa tenacissima,Dactylis glomerata, Festuca spp, Cynosurus elegans, Crataegus laciniata, Arrhenatherum elatius, and Juniperus phoenicea, with the presence ofother species, such as Avena spp., Papaver spp., Trifolium, Hordeum murinum, and Vicia spp. .In the Middle Atlas, the pair rangelands–forest is used throughout the year. At an altitude of1500 2.3The samples were frozen, lyophilized, crushed, and stored at 2.4Dry matter was calculated using the drying method in a stove at 2.5The lipid extract was methylated before analysis. The FAs were converted to fatty acid methyl esters(FAMEs) according to the method described by Ben Moumen et al. (2015) using BF3 at 14 % weight inmethanol. The separation of the FAMEs was performed on an Agilent gas chromatograph (GC)  equipped with an Omega wax capillary column. One gram of fat from each sample is saponified in the presence of 50 2.7cis/trans-C18 : 1, C20 : 1cis/trans-C18:2Saturated fatty acids (SFAs) were the sum of C14 : 0, C15 : 0, C16 : 0, C17 : 0, C18 : 0, C20 : 0, C22 : 0, C23 : 0,and C24 : 0. Monounsaturated fatty acids (MUFAs) were the sum of C14 : 1, C15 : 1, C16 : 1, C17 : 1,2.8Experimental results were expressed as means 33.1trans vaccenic acid and long-chain PUFA An increasingly important aspect of meat quality is its nutritive value, especially fatty acidcomposition . As a matter of fact, according to recent research,individual or group FAs, namely SFAs, MUFAs, and PUFAs, are present in meat; their ratios and sums areemerging as a key factor in nutrition and health . Humannutritionists, public health institutes, and various authorities recommend reducing the consumptionof SFAs and cholesterol . Red meat lipids are widely criticized for their saturatedfatty acid richness . However, some fatty acids in beef and lamb have healthbenefits, such as Table 1 does not show a significant difference between the three studied breeds concerning the IMFcontent (4.6 per 5.73 %); meanwhile, the cholesterol content is significantly high in the BGbreed regardless of its pasture (0.12 %–0.11 %) compared to Ti(0.1 %) and ODj (0.08 %) breeds. The obtained results are in line with the results ofseveral authors  reporting no variation inintramuscular fat content according to genotype. In accordance with the above results, Vacca etal. (2008) and Sinanoglou et al. (2013) have reported a significant effect of genotype oncholesterol content. However, no effect of breed on the cholesterol content was observed but ratheran effect of the diet. Valsta et al. (2005) reported that the cholesterol concentration of meatsrange between 30 and 120 The gas chromatography analysis of the FA composition allowed the separation and the identificationof 26 fatty acids (Table 2). The main LLM fatty acids of the analyzed meats are, in descending order,oleic acid (32.06 %–37.62 %), palmitic acid (20.72 %–25.02 %), stearic acid(12.38 %–13.78 %), and linoleic acid (7.37 %–11.4 %). Saturated fatty acids are oftenconsidered undesirable for our health . However, much research has reportedthe beneficial effect of the same SFA, such as stearic acid and short-chain FAs . Stearic acid, in comparison, is assumed to have no effect on bloodcholesterol levels and is considered as a good predictor of cover fat firmness of carcasses . Overall, our results show the prevailing individual saturated FAs were palmitic acid(PA), stearic acid (SA), and myristic acid (MA). The highest values of SFAs were recorded in BGA(46.53 %) and ODj (42.98 %) sheep meat. This difference is mainly related to the proportion ofpalmitic acid and myristic acid . Ruminant meat ischaracterized by its high content of SFA due to the ruminal bio-hydrogenation phenomenon . However, several studies showed that it is possible to vary the FAcomposition by varying the diet and the rearing system . The differences in SFA content are more due to therearing system than the difference in genotype. Indeed, it can be deduced that the meats of BGA andODj, which were raised in the same rearing system (semi-extensive of Ain Beni Mathar) with animportant concentrate and halfah-based supplementation, are the richest in SFA. This high content ofPA in BG and ODj meats is possibly due to the steppe–pasture diet rich in palmitic acid. In thiscontext, Berrighi et al. (2017) had reported that the chemical composition of the steppe pasturediet contains 24.08 % to 28.48 % of PA. However, our results show no difference between thestudied meats in the percentage of stearic acid. The obtained results of SFAs are lower than thosereported by Margetín et al. (2018) in the meat of Île-de-France sheep breed and are comparableto those reported by Polidori et al. (2017) in the meat of the Fabrianese breed. The SFAs recordedvalues in LL muscle of Ti and BGT comparable to those found by Garcia et al. (2008) in the meatof Patagonian lamb.The MUFA content (Table 3) does not show a significant difference between BGA, Ti, and ODj; on theother hand, a significant lower content was observed for BGT. This difference is notably due to theC18 : 1 content (dominant MUFA), which was considered to be beneficial FAs for decreasing plasmacholesterol and low-density lipoprotein (LDL) . Furthermore, our estimates of enzymatic activity of Delta9-desaturase C18 responsible forconversion of C18 : 0 to their cis-9 monounsaturated, obtained by product-substrate ratios, did notdiffer significantly among the studied meats (data not shown). Thus, in the case of BGT, it isassumed that the difference in C18 : 1 content would not be due to an insufficient synthesis of C18 : 1from C18 : 0 by desaturation but rather to a less important provision in C18 : 0, to a greaterdesaturation of C18 : 1 to C18 : 2, or to a more significant bio-hydrogenation of C18 : 1 to C18 : 0. However, the latter hypothesis should also be ruled out since no difference in the C18 : 0 levelwas observed for the BGT meats compared to the other studied meats. Significant differences, these ratios and indices are important keys when assessing the nutritionalvalue of meat fat in regard to the prevention of CVDs and carcinogenicity . The PUFA Artemisia herba-alba) and halfah (Stipa tenacissima) arenot degraded compared to Ain Beni Mathar breeding area , where they are in an advanceddegradation state . As a result, breeders in Ain Beni Mathar adopt asemi-extensive rearing method with the use of barley and alfalfa hay supplementation. The richnessof the food ration in wormwood  and halfah, which is rich in unsaturatedfatty acids, would be responsible for this difference . Artemisia is rich in active compounds such asantimicrobials that inhibit certain ruminal bacteria, and therefore they would reduce stomachbio-hydrogenation, especially tannins. Thus, they would reduce the oxidative degradation of PUFAsdue to the richness of their essential oils in antioxidants . The ODj sheep meat has asimilar ratio (PUFA Quercus Ilex) rich in PUFA. Various studies have alreadyshown the beneficial effect of a diet rich in oak acorns on the meat quality from different domesticanimal species . Indeed, the incorporationof acorns rich in antioxidants especially in tocopherol makes it possible both to improve the PUFAcontent and to reduce their oxidative degradation, therefore preserving their nutritionalquality . In addition, this decrease in oxidation would improve the meat color. The presence of tannins in acorns would improve thePUFA Therefore, Timahdite and BGT meats show the highest PUFAThe analysis of other calculated lipid indices from the different obtained fatty acids values and which would have a nutritional effect shows significant differences between the four studied meats. From a nutritional point of view, the ratio According to Sinanoglou et al. (2013), AI and TI should not exceed 1. In this study, the AI-obtainedvalues are below the recommended value and are comparable to those reported by Sinanoglou etal. (2013) for Greek breeds (0.55–0.73) and by Costa et al. (2018) for lamb meat inBrazil. However, they are lower than those reported by Della Malva et al. (2016) for Altamuranalamb, by Oriani et al. (2005) for Merino (1.35) and by Margetín et al. (2018) for Île-de-Francelambs (0.97). Timahdite meat has the lower AI ratio but the highest 3.2Principal component analysis (PCA) was conducted to provide an easy visualization of therelationships among fatty acid profile and health lipid indices of the studied meats. This analysiswas used to further explore the above results. The results for PCA applied to parameter values aresummarized in Figs. 1 and 2. Two principal components were extracted from statistical analysis,which explained 63.17 % of the total variance in the data set. The first and second PCs (PC1 andPC2) accounted for 36.65 % and 27.81 % of the variation, respectively. The PC1 was mainlycharacterized by hypocholesterolemic fatty acids, PUFAPCA analysis exposed clearly the breed and geographical area effects on the fatty acid compositionand intramuscular fat quality of the studied meats. Therefore, the breed and geographical areaimpact leads to significant differences in lipid quality (4All obtained results clearly show that significant differences are noted on the lipid composition ofthe longissimus lumborum muscle of the studied breed in Moroccan livestock. Theintramuscular fat content does not seem to be affected in this study either by race or by thefarming method. Analysis of the fatty acid composition and lipid indices shows that meat of Ti breedhas the best lipid profile with its richness in PUFA, its low value of AI, and its high DFAcontent. The richness in PUFA can be explained by its agrosilvopastoral rearing mode or/and by thepasture quality, rather than a difference in racial origin. Among the four studied breeds, the Ti breed is theonly one whose diet is rich in oak acorns. Nevertheless, further studies are needed to confirm theseresults and to ascertain the mechanisms of action of Moroccan medicinal plants such as wormwood andhalfah in eastern Morocco and acorn oak in the Middle Atlas on lipid metabolism in ruminants todetermine the origin of this richness in PUFA.


The cortical code that underlies perception must enable subjects to perceive the world at time scales relevant for behavior. We find that mice can integrate visual stimuli very quickly (<100 ms) to reach plateau performance in an orientation discrimination task. To define features of cortical activity that underlie performance at these time scales, we measured single-unit responses in the mouse visual cortex at time scales relevant to this task. In contrast to high-contrast stimuli of longer duration, which elicit reliable activity in individual neurons, stimuli at the threshold of perception elicit extremely sparse and unreliable responses in the primary visual cortex such that the activity of individual neurons does not reliably report orientation. Integrating information across neurons, however, quickly improves performance. Using a linear decoding model, we estimate that integrating information over 50–100 neurons is sufficient to account for behavioral performance. Thus, at the limits of visual perception, the visual system integrates information encoded in the probabilistic firing of unreliable single units to generate reliable behavior. Animals regularly identify the presence of external stimuli and make decisions based on this evidence within very short time intervals . ReliablIn primates and carnivores, a natural time scale exists for integrating visual information—the fixation duration. Within an intersaccadic duration  and could be involved in integrating relevant visual information. We find that there is only a marginal change in V1 neural activity under these conditions and a majority of neurons show no stimulus-evoked activity even for stimuli in their receptive fields—the average V1 neuron fails to fire a single action potential on a vast majority of the trials.To quantify how well individual neurons perform in discriminating visual stimuli, we developed a simple logistic regression model to quantify the contribution of each neuron to the discrimination task. For short-duration stimuli, we find that the vast majority of recorded units were poor discriminators with only a small fraction (~11%) consistently discriminating the orientation of the stimulus above chance. While consistent, these neurons never improved discrimination of the visual stimulus more than a few percentage above chance. Based on the model, we can project the population requirement for the orientation discrimination task. We find that mice would need to integrate from a few tens to a few hundred neurons from individually unreliable responses to account for the reliable performance of mice in the orientation discrimination task. This constitutes a small fraction (<0.1%) of the total number of neurons available to encode the stimulus in V1 of the mouse , indicatN = 39 adult male and female mice for this study.All procedures were performed with the approval and guidance of the Institutional Animal Care and Use Committee at the University of California, San Diego, CA and at Biogen, Cambridge, MA. We used 2 and an approximately equal luminance across the R, B, and G channels. The screen subtended an angle of 100° × 65° (width × height) with respect to the subject. Mice licked the request port (center port) to display a visual stimulus on the monitor. Mice responded to the displayed stimulus by licking one of the response ports (left and right ports). Correct responses were rewarded with a small droplet of water (~10 μL), while incorrect responses were punished with a timeout (5–20 s). Auditory feedback consisting of beeps of various frequencies and white noise stimuli were also included to further indicate the nature of the responses: correct, incorrect, try again, etc. Throughout the training and testing process, we varied the reward size and the timeout duration to maintain high motivation in the subject.Behavioral training methods were adapted from training systems developed previously for rats . Water-rMice learned visual discrimination tasks over many weeks performing hundreds of trials a day and many thousand trials over the course of the experiment. High performance in the orientation discrimination tasks was achieved by taking the mice through a series of shaping steps. These steps trained naïve mice in using the operant chamber effectively and in learning the structure of a self-directed 2-alternative forced-choice (2AFC) trial before training them on orientation discrimination. We describe the shaping steps for 2 experiments relating to behavior: basic characterization of orientation tuning see  and measWe programmatically capture various facts about each trial performed by subjects. This allowed us to perform a quantitative assessment of behavioral performance. In our tasks, the subjects have complete control over when trials are requested and when they respond to trial requests. For analysis, we excluded trials that were completed within 50 ms. Such fast responses would have required unattainable motor speeds and likely indicated water-clogged ports due to incomplete reward consumption. This condition excluded <0.1% of the trials. We further excluded trials that took >5 s. This was to ensure that we only counted trials where subjects were highly motivated to perform the task, excluding trials where subjects were distracted or in a low motivational state. The fraction of trials rejected due to large Reaction times (RTs) did not exceed 3.05% in any of our subjects and averaged 1.18 ± 0.91% (mean ± standard deviation [SD]). We measured confidence intervals (CIs) on the performance  using the Clopper–Pearson method  and the P is the psychophysical performance function and In the experiments where we vary various features of the sensory input (contrast and duration), we model subject performance as a function of the strength of the stimulus:s is the strength of stimulus, m is the stimulus strength at half-maximum performance, ω is the width of the psychometric function (stimulus strengths where the psychometric function changes), λ is the lapse rate, and γ is the guessing rate. In our analysis of contrast threshold, we define stimulus contrast at half-maximum performance as the threshold contrast (denoted as υ), and in our analysis of integration times, we define the stimulus duration at half-maximum performance as the threshold integration time (denoted as τ). Since all our behavioral data were obtained with a 2AFC trial structure, we set γ to 0.5 in our analyses. Consistent with this assumption, when subjects were presented with trials having little or no information about the correct response (zero-contrast stimulus or very-low–duration stimulus), subjects’ performance was no better than chance. We then identified those parameters  that best fit our data. To identify these parameters, we used constrained maximum likelihood techniques—techniques that maximize the likelihood of the given data for some estimates of the unknown parameters, subject to constraints—to create point estimates. These analyses forced guessing rates to 0.5 (50%) and assumed that priors for the lapse rates (λ) followed a beta distribution with shape parameters  while the priors for m and ω were flat through the stimulus range. We sampled from the posterior distribution using Markov chain Monte Carlo techniques and used the 95-percentile range of the marginal of the posterior distribution of the fitted parameters as the CI. Our analyses are based on previously described open-source methods (psignifit) (where ignifit) . In expeignifit) C.The perceptual thresholds measured varied from animal to animal. We fit psychometric tuning curves to the responses of individual animals and report the ranges of the fit parameters where applicable. When we report aggregate values, we used the values fit from the responses of an average subject. We simulated the average subject from the responses of all the animals in the population. However, different subjects performed different numbers of trials based on individual trial rates. Eight subjects were included in the contrast threshold estimation study where subjects performed between 2803 and 6078 trials averaging 4255 ± 1192 (mean ± SD). Six subjects were included in the visual integration study where subjects performed between 16 098 and 54 654 trials averaging 32 051 ± 13 896 (mean ± SD). To ensure equal weight for each animal in measuring aggregate fit parameters, we sampled the same number of trials from each subject randomly across stimulus conditions. We created the average subject by concatenating these sampled responses. To ensure that the sampling process did not bias estimates, we performed the resampling process 1000 times. We report the mean of the most likely estimates across these 1000 resamples for the average subject.We used standard surgical techniques to implant NeuroNexus probes  into V1. Adult mice were anesthetized under isoflurane (2.5% [v/v] for induction and 1.5% [v/v] for maintenance). After subjects were anesthetized, the fur from the top of the head was shaved and the mouse was injected with atropine to minimize secretion (0.3 mg/kg) and dexamethasone to prevent inflammation (2.5 mg/kg). Subjects were then placed on a stereotaxic frame . The scalp over V1 was removed using surgical scissors, and the skull dried. A small (~0.5-mm) craniotomy was made over the monocular region of V1, and NeuroNexus probes were inserted  into the craniotomy. The open craniotomy was covered with silicone gel. A second adjacent craniotomy was made over the olfactory bulb, and ground/reference screws were installed here. The exposed skull surface was then closed using dental cement. A custom-designed head bar was also installed to enable head fixing for later experiments. Subjects were then removed from the stereotaxic frame and allowed to recuperate in a heated chamber and injected with buprenorphine for postoperative pain management . All V1 recordings began at least 5 days postsurgery. In a small number of animals, chronic NeuroNexus probes were installed along with movable electrode drives to sample neural populations at different depths for each session.The surgical procedure was identical to that of the chronic probe surgery in all respects except that no craniotomy was performed over V1. The skull surface was cleaned, and the area over V1 was covered with a thin layer of cyanoacrylate-based glue and allowed to dry completely. The animal was allowed to recover for 5 days after surgery.Channelrhodopsin-2 (ChR2) was targeted into parvalbumin-positive (PV+) fast-spiking interneurons within V1 through 1 of 2 ways: 1) PV-cre (JAX:0080609) subjects were injected with 200–400 nL of AAV2-Flex-ChR2-tdTomato (UPenn Vector core Cat# AV-9-20297P) at various depths using a Nanoject II (Drummond Scientific) bilaterally over V1, and 2) PV-cre animals (JAX:0080609) were crossed with Flex-ChR2 (JAX:024109) and F1 progeny positive for both genes were selected for behavior. Fiber-optic cannulae (MFC_480/500-0.63_3mm_ZF1.25(G)_B60, Doric Lenses Inc.) were implanted into the open craniotomies over V1 such that the fiber terminus lay over the exposed brain surface. The open skull was covered with dental cement, and the mouse was removed from the stereotaxic frame and set aside for recuperation. As a light stimulation negative control, 2 subjects underwent a sham injection. These subjects (PV-cre:0080609) were implanted with fiber-optic cannulae over V1 without viral injection.2.Subjects implanted with fiber-optic cannulae were allowed to recuperate for 5 days, after which they were reintroduced into the behavior chamber. We established baseline behavioral performance in the orientation discrimination task for ~ 1 week before attaching the animal to a fiber-optic light source. Transistor-Transistor Logic (TTL) pulses from the behavior computer controlled the LED light source  through an LED driver . This light entered the behavior arena via an optical commutator , then split into two through a 1 × 2 branching fiber-optic patch cord (BFP(2)_480/500/900-0.63_0.3m_FCM-2xZF1.25(F), Doric Lenses), and mated with the cannulae with a plastic sleeve. Based on prior estimates of stimulus latency within V1 , we exteSubjects that underwent electrode implantation surgery were allowed to recuperate for 5 days. We acclimated subjects to being head fixed for 2 days before recording from V1 neurons. Subjects were head fixed by screwing the custom-designed head bar to a mating bar. The mating bar was then attached to the recording rig with the mouse placed over a Styrofoam ball suspended in air to allow free range of motion  or over z-scored to identify epochs of high engagement (z > 0) and low engagement (z ≤ 0). In these animals, running speed was measured using an optical motion sensor (PWM3360) placed against the outer surface of the wheel. Motion data were captured by an Arduino device programmed with custom code, converted to analog voltages , and synchronized with the neurophysiological recording using the Omniplex System (Plexon Inc.). In our hands, epochs of high locomotion coincided with epochs of larger pupil sizes. Thus, we used pupil size as the sole measure of arousal.In sessions where pupillometry was performed, videos of the mice’s pupils were captured with a Stingray camera with infrared (IR) filter removed while recording the mouse’s eyes under IR illumination (850 nm) using a Computar MLH-10X zoom lens. Visible light was excluded from the camera using a spectral filter . Eye tracking was synchronized with neural recording using the Cineplex system . To measure pupil size from the video, a deep learning algorithm was trained on sample eye-tracking videos from multiple subjects across multiple days (Deeplabcut 2.0 ). Based Subjects were allowed to recover for 5 days postsurgery and were acclimated to the Styrofoam ball for at least 2 days before the recording. On the day of the recording, subjects were lightly anesthetized, a small craniotomy was performed over V1, and the exposed brain was covered with silicone gel. The animal recovered from anesthesia for at least 2 h before recording.Raw neural data were filtered between 300 and 10 000 Hz using a zero-phase digital filter. Open source libraries  spikede. For eacWe synchronized spiking responses in V1 with stimulus presentation using TTL pulses. Unless otherwise specified, on each stimulus presentation, we extracted the total number of spikes in a time window that started with the visual stimulus onset and extended to 500 ms after onset. We chose to analyze this time interval for 3 reasons. 1) V1 responses do not begin immediately after stimulus onset. Capacitive charging effects and line delays cause a response latency of ~ 50–100 ms . 2) V1 rSpike rates for each neuron were considered one at a time. We first excluded the firing rates for trials without stimuli . The firing rates for the remaining trials were randomly assigned to “Training” (70%) and “Test” (30%). The data in the Training set were used to fit a logistic regression model (statsmodels v0.9.0 in Python 3.6) and logistic regression coefficients obtained for each neuron. This regression coefficient was used to test the model on the Test dataset . We repeated this process 100 times with a different subset of trials belonging to the Training and Test datasets. The results of regression included the probability that the extracted coefficient was significantly different than 0. If this probability was high (>95%) in at least 70 out of 100 different splits, the unit was considered a consistent predictor . For fitting performance across populations, we used all the spike rates in a session and performed the regression in a similar fashion. The decoding performance of a single unit or a population was the average performance across the 100 splits.For each of the neurons in our dataset, we tabulated the spike count responses according to the stimulus conditions tested in our study . To create a virtual session of a given population size from our dataset, we first chose a random subset of that size from the overall population with replacement with each neuron being equally likely to be included in the subpopulation. Responses for each neuron were then simulated from the corresponding response table making sure to only sample from the responses of that neuron for that stimulus condition. We used the previously calculated regression coefficients  as the regression coefficient for the simulated neuron. The orientation of the stimulus was then predicted based on these independent regressors and compared against the input orientation. This was repeated 1000 times to provide an estimate of the performance of a populationof that size.c = 1.0 greater than performance at c = 0.15 for 8/8 mice). We fit the performance of mice across contrasts to a logistic regression curve . This was true for high-contrast stimuli (c = 0.15), all subjects perform significantly above chance for stimuli lasting 50 ms at low contrast. At higher contrast (c = 1), all subjects were significantly above chance for a stimulus duration of 16 ms, the shortest stimulus duration tested. To precisely measure the dynamics of integration, we fit a logistic regression curve to the stimulus duration versus performance curve  averaging 24 ± 12 ms  averaging 54 ± 16 ms . Thus, subjects require very short stimulus durations—significantly shorter than the typical intersaccade duration—to perform significantly above chance in an orientation discrimination task. The average subject had a threshold integration time of 12 ms at high contrast , an order of magnitude lower than the mean reaction times (~1 s) from the reaction time task described earlier.To precisely measure the time window over which mice effectively integrate information, we tested subjects trained in orientation discrimination to perform trials where the maximum duration of a stimulus available for the subject is systematically controlled (see Task Sequence and Parameters of Stimuli for Behavior). After this maximum duration (denoted “stimulus duration”), the screen changes to a gray screen and awaits response from the subject A. This tP = 0.85, Mann–Whitney–Wilcoxon U test). Similarly, plateau performance for low-contrast stimuli (mean ± SD = 0.74 ± 0.08) is comparable with the performance of subjects for low-contrast stimuli (mean ± SD = 0.67 ± 0.1) in the reaction time task . This indicates that subjects have integrated as much of the information from the visual stimulus as possible within ~ 100 ms to guide their behavior.Our assessment of the total integration time indicated that it was between 20 and 400 ms for high-contrast stimuli and between 55 and 250 ms for low-contrast stimuli E. The toP < 0.05, Mann–Whitney–Wilcoxon U test). We confirmed that the loss in performance was not due to the distracting influence of the blue light by performing identical experiments in a small cohort of sham animals (n = 2) that did not express ChR2 in PV+ inhibitory interneurons (U test). Thus, activity of neurons in V1 is necessary for mice to carry out this orientation discrimination task.To test if V1 neurons are required for orientation discrimination, we expressed blue-light–sensitive ChR2 in PV+ inhibitory interneurons . We testN = 9 mice) were recorded in naïve animals that had no exposure to the behavioral arena and 16 (N = 6 mice) were recorded in animals that had prior experience in the orientation discrimination task and had threshold performance in the simple orientation discrimination task . We denote these as “fast-spiking” and “regular-spiking” neurons respectively. We expect that these clusters are enriched in fast-spiking, PV+ interneurons and regular-spiking excitatory neurons, respectively.To understand the neural basis for fast visual integration, we recorded from neurons across the layers of V1 in subjects running on a Styrofoam ball or on a vertical wheel while passively viewing stimuli of various contrasts, durations, and orientations A. VisualAs expected from neurons in V1, a large fraction of neurons showed significant orientation tuning C. For a −4, KS test vs. uniform null hypothesis). We note that a small population of neurons (N = 168) show very high orientation selectivity . These neurons have significantly lower firing rates compared with the rest of the population . The high OSI was due to an extremely low firing rate for the orthogonal orientation, but each of these neurons had consistent orientation tuning—the SD of Jackknife estimates of orientation preference was less than 20° in all high-OSI units with a median of 2.5°. Furthermore, putative regular-spiking neurons had a higher mean OSI than fast-spiking neurons . Thus, as shown previously, we find that neurons in V1 are orientation tuned and are ideally poised to encode the stimulus orientation to direct the animal’s behavior.The orientation selectivity index spanned values from 0 to 1 with a mean selectivity of ~ 0.31 C, while f = 37% at c = 0 vs. f = 43% at c = 0.15, and f = 37% at c = 0 vs. f = 44% at c = 1). The difference between the fraction of responsive neurons was not significantly different across any of the stimulus conditions . Thus, for such short stimuli, merely looking at the number of active neurons would not indicate the presence or absence of the stimulus. These results indicate that, for stimuli that drive reliable behavior, stimulus onset increases the fraction of responsive V1 neurons by at most 8%.Next, we characterized the population responses of simultaneously recorded neurons to stimuli that last a short duration (≤200 ms). We recorded between 2 and 54 neurons see E spike cJ). To assess the influence of contrast on spike probability, we calculated the fraction of trials that elicited at least one spike during the time that spanned the stimulus presentation and extended 100 ms after stimulus offset. The spike probability changed as we varied contrast at the preferred orientation (J). Trials where the neuron failed to respond with a single spike are denoted with gray lines across the duration of the trial. The neuron responds unreliably even for high-contrast stimuli  while 3) maximizing the number of consistent predictors  and Test (30%) sets A as welledictors C.P < 10−6, Mann–Whitney–Wilcoxon U test). This subpopulation 1) was enriched in single units compared with the overall population , 2) included units with firing rates from 0.1 to > 80 Hz , and the overall distribution of orientation preference was significantly different between the consistent and overall populations . Consistent units included both regular- and fast-spiking units in approximately the same proportion that they were present in the overall population. Both categories discriminated the stimulus to the same degree .Individual neurons encoded the orientation of the stimulus poorly. The performance of individual neurons across all stimulus conditions varied from 0.46 to 0.64 D, compar0 Hz see , 3) show0 Hz see B, and 4)0 Hz see F. SpecifP < 0.001, Agresti–Caffo statistics), 2) the average performance of the consistent population was significantly higher , and 3) a small population of units (10/781) performed better than the performance of the animal  were captured in a subset (781 out of 2373) of units see . Identicimal see . DecodinP = 0.81), it correlated well with the number of consistent neurons recorded in the session (P < 10−3). There was a rapid increase in stimulus prediction performance with recruitment of additional neurons indicating a high sensitivity to integrating information from even a relatively small number of neurons.Since even the best performing single units were poor predictors of stimulus orientation, we next explored how integration across relatively unreliable units might affect stimulus prediction. To determine how decoding of stimulus improves with integration of information from multiple neurons, we used the simultaneously recorded spike rates in all the neurons recorded in a session to fit a multidimensional logistic regression similar to the method employed for the single neuron fits . The threshold contrast measured (~15%) was similar to thresholds measured earlier in mice  to perform visually driven tasks. This is consistent with recent work in mice . MoreoveThe ability of mice to discriminate orientation requires V1, as their performance is compromised by optogenetic inhibition of V1 responses. This is consistent with the known response properties of V1 neurons  and withN = 6 mice, 16 sessions) that had experience with the orientation discrimination task. We found that training changed neither the decoding performance  while simultaneously recording pupil diameter and running speed (z-score > 0) and compared it with intervals of low engagement (pupil diameter z-score ≤ 0). This split the data to include 50% of the trials in the high-arousal subset  Logistic regression of neural responses provided sensory evidence, which 2) on a random fraction of trials , was corrupted by the cognitive process ; Biogen Inc, Cambridge, MA, USA; and NARSAD Young Investigator Grant (to A.C.M.).We are grateful to Pamela Reinagel for support and feedback relating to this manuscript as well as for foundational work on automating visually-directed tasks in rodents. We would like to thank Daniel Bishop, Madeleine Ciobanu, Yash Gandhi, Ashley Gutierrez, and Julia Ramirez for assistance with behavior experiments and Robert Recatto for help with analysis.CC_Supplemental_Resubmission_r2_bhz147Click here for additional data file.


Automated testing of HIV serology on clinical chemistry analysers has become common. High sample throughput, high HIV prevalence and instrument design could all contribute to sample cross-contamination by microscopic droplet carry-over from seropositive samples to seronegative samples resulting in false positive low-reactive results. Following installation of an automated shared platform at our public health laboratory, we noted an increase in low reactive and false positive results. Subsequently, we investigated HIV serology screening test results for a period of 21 months. Of 485 initially low positive or equivocal samples 411 (85%) tested negative when retested using an independently collected sample. As creatinine is commonly requested with HIV screening, we used it as a proxy for concomitant clinical chemistry testing, indicating that a sample had likely been tested on a shared high-throughput instrument. The contamination risk was stratified between samples passing the clinical chemistry module first versus samples bypassing it. The odds ratio for a false positive HIV serology result was 4.1 (95% CI: 1.69–9.97) when creatinine level was determined first, versus not, on the same sample, suggesting contamination on the chemistry analyser. We subsequently issued a notice to obtain dedicated samples for HIV serology and added a suffix to the specimen identifier which restricted testing to a dedicated instrument. Low positive and false positive rates were determined before and after these interventions. Based on measured rates in low positive samples we estimate that before the intervention, of 44 117 HIV screening serology samples, 753 (1.71%) were false positive, declining to 48 of 7 072 samples (0.68%) post-intervention (p<0.01). Our findings showed that automated high throughput shared diagnostic platforms are at risk of generating false-positive HIV test results, due to sample contamination and that measures are required to address this. Restricting HIV serology samples to a dedicated platform resolved this problem. In high-prevalence settings like South Africa, this requires a single screening test followed by one or two confirmatory tests for samples with reactive screening results Reviewers' comments:Reviewer's Responses to QuestionsComments to the Author1. Is the manuscript technically sound, and do the data support the conclusions?The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes**********2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes**********3. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.The Reviewer #1: Yes**********4. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: Yes**********5. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: This paper provides good evidence for how false positive HIV serology results can be generated on automated analysers that perform a combination of chemistry, immunology and serology tests frequently used in clinical pathology laboratories. The key finding was the 4-fold increase in likelihood that a sample would test HIV positive if it had first passed through the chemistry module of the analyser (HIV serology tested after creatinine was tested). The authors also demonstrate the effectiveness of measures to reduce sample contamination, in particular use of a dedicated sample tube for HIV serology testing. Recognition of this problem is very important, especially where these instruments are used in areas of high HIV sero-prevalence. I recommend publication, but there are a number of aspects of the manuscript that require fixing. I found the write up of the results particularly difficult to followAbstract:Line 24: Alternative lead in sentence: Automated testing of HIV serology on clinical chemistry analysers has become common.Line 24: “high throughput, high HIV….”Line 38: “repeat independent testing on follow up samples of 485…”Lines 40-42: “The odds ratio for generating a false positive HIV serology result was 4.1 (95%CI:1.69-9.97) when creatinine was tested before HIV serology on the same sample…”Introduction:P3, line 51; full stop after “serological testing”P3, line 80: delete “in order…recently”. Replace with “We issued a notice…”P3 line 86: Dedicated sample labelling: “ to enforce dedicated sample testing for HIV serology a suffix (referred to hereafter as “Q”suffix) is added to the barcode sample label on all…”I would prefer the authors to refer to the use of a dedicated sample tube, rather than “Q suffix”  throughout the manuscript, but I don’t feel too strongly about it.Remove references 11 and 12 – these are not documents available to journal readers.Line 92: “After requests for HIV serology are not permitted on the…”P4:Study aims: (switch around)1) To assess factors associated with LP&E serology2) To investigate the impact of measures to ensure the use of a dedicated sample on the overall…”Methods:Under data retrieval and analysis:After the first lead-in sentence, switch the order of paragraphs as followssection from line 136 to 152 should come before lines 121-135.Line 128: “…would not have been at risk of contamination from this source”Line 140 : replace “these samples” with “initial results” and “negative” with “false positive”Line 142: “the sample was classified as true positive”Line 145: “It is expected that not all LP&E results have…”Results:158: “…before and after the dedicated sample tube intervention…”162-175: this section is very hard to follow. Please rewrite focussing on the main findings you want to highlight.I am confused by some of these results. In table 1 and in the text it is stated that a total of 3591 samples were concurrently tested for creatinine “after “Q suffix” implementation (in 2563 creatinine was tested first and in 1028 it was tested second). How can this be? The Q suffix was supposed to ensure that the sample was only tested for HIV. These numbers should all be “0”.It seems to me that the important figures to compare are the rates of LP&E between the samples where creatinine was tested first, vs where it was tested 2nd . i.e. 3.32% vs 0.52%Lines 182-191: again very difficult to follow.Table 1Title should read: “Summary of low positive and equivocal HIV antibody rates in samples tested before and after introduction of a dedicated sample tube for HIV serology testing.The figures (numbers) for LP&E results: please amend or make it clear what the numbers in columns 3 and 4 (rows 7-12) refer to.Middle section of the table:Column 1 row one: LP&E samples with a repeat test (or positive samples with a repeat test)Bottom section: (extrapolation data) adds no value. I would removeFigure 1:No legend is given. I would suggest “ A downward trend in percentage of LP&E samples was observed with serial introduction of measures to reduce sample contamination on the analyser.”Figure 2:Legend: Bar chart shows percentage of LP&E results over 4 month intervals from September 2017 to May 2019.Discussion:Rewrite the first paragraph with a lead in sentence contextualising the problem. Highlight the main findings. Don’t restate all the results in the first paragraph.References:The following references are not available to journal readers. Either provide a URL or remove from the list: 7, 8, 10, 11, 12,Provide URL for 4 and 20**********what does this mean?). If published, this will include your full peer review and any attached files.6. PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool,  7 Dec 2020Thank you for considering our manuscript and taking time to review it. Herewith, find our response to the reviewer:“This paper provides good evidence for how false positive HIV serology results can be generated on automated analysers that perform a combination of chemistry, immunology and serology tests frequently used in clinical pathology laboratories. The key finding was the 4-fold increase in likelihood that a sample would test HIV positive if it had first passed through the chemistry module of the analyser (HIV serology tested after creatinine was tested). The authors also demonstrate the effectiveness of measures to reduce sample contamination, in particular use of a dedicated sample tube for HIV serology testing. Recognition of this problem is very important, especially where these instruments are used in areas of high HIV sero-prevalence. I recommend publication, but there are a number of aspects of the manuscript that require fixing. I found the write up of the results particularly difficult to follow”• We clarified many aspects of the results write up.‘Abstract:Line 24: Alternative lead in sentence: Automated testing of HIV serology on clinical chemistry analysers has become common.Line 24: “high throughput, high HIV….”Line 38: “repeat independent testing on follow up samples of 485…”Lines 40-42: “The odds ratio for generating a false positive HIV serology result was 4.1 (95%CI:1.69-9.97) when creatinine was tested before HIV serology on the same sample…”’• These changes have been made.• “Automated testing of HIV serology on clinical chemistry analysers has become common. High sample throughput, high HIV prevalence and instrument design could all contribute to sample cross-contamination by microscopic droplet carry-over from seropositive samples to seronegative samples resulting in false positive low-reactive results.”• “Of 485 initially low positive or equivocal samples 411 (85%) tested negative when retested using an independently collected sample.”• “The odds ratio for a false positive HIV serology result was 4.1 (95% CI: 1.69-9.97) when creatinine level was determined first, versus not, on the same sample, suggesting contamination on the chemistry analyser.”‘Introduction:P3, line 51; full stop after “serological testing”P3, line 80: delete “in order…recently”. Replace with “We issued a notice…”’• These changes have been made. • “Laboratory-based diagnosis of Human Immunodeficiency Virus (HIV) infection in adults relies on 4th generation serological testing.”• “Dedicated sample requirement for HIV testing: We issued a notice to clinicians requiring them to send separate blood samples when HIV testing is requested, to reduce the risk of contamination on pre-analytical instrumentation and high throughput chemistry analysers[15].”“P3 line 86: Dedicated sample labelling: “ to enforce dedicated sample testing for HIV serology a suffix (referred to hereafter as “Q”suffix) is added to the barcode sample label on all…”I would prefer the authors to refer to the use of a dedicated sample tube, rather than “Q suffix”  throughout the manuscript, but I don’t feel too strongly about it.Remove references 11 and 12 – these are not documents available to journal readers.”• We concur and have removed reference to “Q” suffix. We have replaced it with reference to a dedicated separate sample or dedicated sample intervention.‘Line 92: “After requests for HIV serology are not permitted on the…”’• These changes have been made.• “After requests for HIV serology are no longer permitted on samples already processed on the shared interdisciplinary module.”“P4:Study aims: (switch around)1) To assess factors associated with LP&E serology2) To investigate the impact of measures to ensure the use of a dedicated sample on the overall…”’• These changes have been made.• “1) To assess the factors that were associated with low positive and equivocal (LP&E) and FP HIV serology testing. 2) To investigate the impact of the dedicated sample on the overall prevalence of LP&E and FP results.”“Methods:Under data retrieval and analysis:After the first lead-in sentence, switch the order of paragraphs as followssection from line 136 to 152 should come before lines 121-135.Line 128: “…would not have been at risk of contamination from this source”Line 140 : replace “these samples” with “initial results” and “negative” with “false positive”Line 142: “the sample was classified as true positive”Line 145: “It is expected that not all LP&E results have…”’• These changes have been made.• “As molecular testing is often required in the work-up of discordant serology, the proportion of LP&E results that had a fresh independently collected sample for subsequent serological or molecular HIV test was assessed. The combined outcome of the subsequent independently collected sample’s test results was then classified as negative, positive, and indeterminate. The median time between the original and subsequent fresh independently collected test result was used to indicate clinician response to LP&E results.When a subsequent independently collected serology test or molecular test gave a negative result, initial results were classified as FP. When a subsequent independently collected follow-up serology result was high positive and confirmed on the confirmatory assay or the molecular test result was positive, the sample was classified as true positive. If subsequent testing produced an LP&E result like the original, with no additional molecular or serology testing results, samples were classified as indeterminate.The FP rate was defined as the percentage of LP&E samples with a negative definitive result, as clinicians are required to send independently collected follow-up samples if an LP&E result is generated. It is expected that not all LP&E results have testing of subsequent independently collected follow-up samples due to the reliance on clinicians to provide such samples. Therefore, the false positive rate was extrapolated based on the LP&E results with known subsequent independently collected follow sample testing results. This rate was then used to calculate the prevalence of true positives, the true positive predictive value (PPV) and the specificity. The PPV calculated from the prevalence and the manufacturer’s claimed specificity was used as comparison.The outcome of confirmatory testing was described as overall proportion of FP results that generated a discordant confirmatory test result on a separate platform, bearing in mind that the sample could have been contaminated before confirmatory testing.Results for HIV serology including HIV-S and HIV confirmatory (HIV-C) testing and subsequent tests linked to the same individual were retrieved from the NHLS TrakCare laboratory information system. As a single sample identifier (ID) is generated when different sample tubes linked to the same request form are registered, the same sample-ID for different tests indicates concurrent testing. However, it does not necessarily indicate testing of the same tube on a different instrument. Therefore, in order to identify samples at risk of contamination, we first used CRT registered on the same sample ID of HIV-S samples as a screen for being at risk of clinical chemistry module processing and contamination. Second, we used samples being tested on the clinical chemistry module first, rather than second to further stratify contamination risk. We could be confident that samples with CRT results released after HIV-S results did not pass through the clinical chemistry module first and would not have been a source of contamination risk. LP&E and FP results were stratified into samples with concurrent CRT testing vs. those without, CRT results released first (CRT1st) vs. CRT results released second (CRT2nd), and before and after the “Q” suffix implementation. These comparisons included prevalences and the calculation of odds ratios (OR) to determine the risk of generating a LP&E and FP result within these strata. Fischer’s Exact Test was used to assess whether there were significant associations with LP&E or FP results, respectively before and after dedicated sample intervention, whereas the Mantel-Haenszel test was used to assess whether these associations were independent of the dedicated sample intervention.”‘Results:158: “…before and after the dedicated sample tube intervention…”’• These changes have been made.• “Table 1 separates these values into totals found before and after the dedicated sample intervention and illustrates the proportional changes seen.”“162-175: this section is very hard to follow. Please rewrite focussing on the main findings you want to highlight.• We have made extensive changes to the article and have attempted to more clearly highlight the main aims of the study. I am confused by some of these results. In table 1 and in the text it is stated that a total of 3591 samples were concurrently tested for creatinine “after “Q suffix” implementation (in 2563 creatinine was tested first and in 1028 it was tested second). How can this be? The Q suffix was supposed to ensure that the sample was only tested for HIV. These numbers should all be “0”.• We have clarified that these samples had creatinine testing requested on them, but due to the intervention a dedicated separate sample was processed. These results illustrate, despite concurrent creatinine testing being requested, there was a significant decline in the number of false positive results after the intervention.“It seems to me that the important figures to compare are the rates of LP&E between the samples where creatinine was tested first, vs where it was tested 2nd . i.e. 3.32% vs 0.52%”• We agree and have emphasised these findings.‘Lines 182-191: again very difficult to follow”’• We have made significant changes to the text here.“Table 1Title should read: “Summary of low positive and equivocal HIV antibody rates in samples tested before and after introduction of a dedicated sample tube for HIV serology testing.The figures (numbers) for LP&E results: please amend or make it clear what the numbers in columns 3 and 4 (rows 7-12) refer to.Middle section of the table:Column 1 row one: LP&E samples with a repeat test (or positive samples with a repeat test)Bottom section: (extrapolation data) adds no value. I would removeFigure 1:No legend is given. I would suggest “ A downward trend in percentage of LP&E samples was observed with serial introduction of measures to reduce sample contamination on the analyser.”Figure 2:Legend: Bar chart shows percentage of LP&E results over 4 month intervals from September 2017 to May 2019.”• Suggestions have been accepted and necessary changes to the table and text have been made.• “Table 1. Summary of low positive and equivocal (LP&E) HIV antibody rates in samples tested before and after introduction of a dedicated sample tube for HIV serology.HIV-S: HIV Screening Serology Results, CRT: Creatinine Testing, LP&E: Low Positive and Equivocal Results, HIV-S Only Total: Total Number of HIV Screening Serology Results without Concurrent Creatinine Testing HIV-S Only LP&E: Total Number of Low Positive and Equivocal HIV Screening Serology Results without Concurrent Creatinine Testing HIV-S/CRT total: Total Number of HIV Screening Serology Result with Concurrent Creatinine Testing, HIV-S/CRT LP&E: Low Positive and Equivocal Results Generated by HIV Screening Serology with Concurrent Creatinine Testing CRT1st Total: Total number of HIV Screening Serology Results with Concurrent Creatinine Result Released Before HIV Result, CRT1st LP&E: Number of Low Positive and Equivocal HIV Screening Serology Results with Concurrent Creatinine Result Released Before HIV Result CRT2nd Total: Total number of HIV Screening Serology Results with Concurrent Creatinine Testing with Creatinine Result Released after HIV Result, CRT2nd LP&E: Number of Low Positive and Equivocal HIV Screening Serology Results with Concurrent Creatinine Testing with Creatinine Result Released after HIV Result*False positive samples”• “Fig1. Low positive and equivocal results monthly proportions displayed over the 21 month study period. A downward trend in percentage of LP&E results was observed with serial introduction of measures to reduce sample contamination on the analyser.”• “Fig2. False positive proportions in groups with the last group being after the dedicated sample intervention. Bar chart shows the percentage of LP&E results over 4 month intervals from September 2017 to May 2019.“Discussion:Rewrite the first paragraph with a lead in sentence contextualising the problem. Highlight the main findings. Don’t restate all the results in the first paragraph.”• We have rewritten the first paragraph and improved the flow of the text.• “The demand on clinical laboratories to produce hundreds to thousands of results every hour has led to the utilisation of automated high throughput analysers. As evidenced in our study, this can increase the risk of sample cross-contamination. A sample had a significant risk of producing a FP result if previously exposed to the clinical chemistry (CRT) analyser (OR of 2.20 95% CI: 1.79-2.69). The most significant association pointing to sample contamination was the increased risk seen in samples with CRT performed before HIV-S. Samples had an OR of 7.04 (95% CI: 3.33-14.87) for testing LP&E vs negative when comparing CRT1st vs. CRT2nd. If CRT was tested second it could not have been contaminated by the clinical chemistry analyser before being tested for HIV. The success of the dedicated sample intervention is demonstrated by the subsequent reduction of LP&E results by 55% and FP results by 60%. A significant reduction of 82% in the LP&E results with concurrent CRT testing after the dedicated sample intervention was observed. Of the LP&E samples that had subsequent tests, 85% were negative, supporting that the large majority of LP&E results are FP. Moreover, same sample confirmatory testing on a different platform averted only 42% of false positives.”• “References:The following references are not available to journal readers. Either provide a URL or remove from the list: 7, 8, 10, 11, 12,Provide URL for 4 and 20”• Changes were made.AttachmentResponse to Reviewers.docxSubmitted filename: Click here for additional data file. 28 Dec 2020HIV false positive screening serology due to sample contamination reduced by a dedicated sample and platform in a high prevalence environmentPONE-D-20-03947R1Dear Dr. Linström,We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.Within one week, you’ll receive an e-mail detailing the required amendments. 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BCR-ABL1-negative myeloproliferative disease prefibrotic primary myelofibrosis (pre-PMF) have a certain risk for progression to myelofibrosis. Accurate risk estimation for this fibrotic progression is of prognostic importance and clinically relevant. Commonly applied risk scores are based on clinical, cytogenetic, and genetic data but do not include epigenetic modifications. Therefore, we evaluated the assessment of genome-wide DNA methylation patterns for their ability to predict fibrotic progression in PMF patients.Patients suffering from the n = 12) or stable disease over several years (n = 10) using the 850 k EPIC array from Illumina. The DNA methylation classifier constructed from this data set was validated in an independently measured test set of additional 11 bone marrow trephines . Hierarchical clustering of methylation β-values and linear discriminant classification yielded very good discrimination between both patient groups. By gene ontology analysis, the most differentially methylated CpG sites are primarily associated with genes involved in cell–cell and cell–matrix interactions.For this purpose, the DNA methylation profile was analyzed genome-wide in a training set of 22 bone marrow trephines from patients with either fibrotic progression (In conclusion, we could show that genome-wide DNA methylation profiling of bone marrow trephines is feasible under routine diagnostic conditions and, more importantly, is able to predict fibrotic progression in pre-fibrotic primary myelofibrosis with high accuracy. BCR/ABL1-negative myeloproliferative neoplasms and is often characterized by a bi-phasic course of disease. In the initial phase, the disease manifests itself with thrombocytosis and granulocytosis without bone marrow fibrosis [Primary myelofibrosis (PMF) belongs to the fibrosis . This phfibrosis . Progresfibrosis , 5, the So far the identification of molecular risk factors with overall survival or blastic transformation as endpoints has focused on karyotype, gene expression, and genetic alterations –8. RecenTherefore, we analyzed the DNA methylation profile in bone marrow trephines from patients with pre-fibrotic PMF at diagnosis with no indications for excess of blasts or marrow fibrosis (EB0 and MF0) who later developed myelofibrosis (MF2 or 3). The results were compared with the DNA methylation patterns in bone marrow trephines from prefibrotic PMF patients who showed stable disease for at least four years after diagnosis. For this purpose, the most recent version of the Illumina DNA methylation array was employed (850 k EPIC array).In a first step, we analyzed 4 samples using the Illumina EPIC array in order to figure out whether the amount and quality of genomic DNA extracted from the routinely processed, fixed, decalcified, and embedded bone marrow trephines in our institution provide a reliable and evaluable signal output.Altogether, 658,746 out of 865,859 CpG sites were evaluable . That means, only 14% of the CpG under showed a signal to noise ratio too low for proper evaluation, demonstrating the feasibility of EPIC DNA methylation array analyses of FFPE bone marrow trephines. This offers the unique opportunity to combine molecular studies with histomorphological examination. Other groups could demonstrate this for different tissue types as well –21.p-value cutoff of 0.05 [The amount of genomic DNA used for DNA methylation analysis and the age of the sample  could be excluded as confounding factors in our series of altogether 33 samples  was used . A test set of additional 11 samples  was used in a second step for independent confirmation of the methylation classifier.Unsupervised hierarchical clustering of the 1000 most differentially methylated CpG sites yielded a very good separation of both patient groups. Figure Further analysis of the differentially methylated CpG sites revealed that smaller subsets could be used to discriminate samples displaying fibrotic progress from patients with stable disease, the minimum being 6 CpG sites  failed the stringent quality metrics evaluation process. Three out of these 7 samples missed the thresholds of various parameters only minimally, thereby representing suboptimal samples which can be encountered under routine diagnostic conditions.Additional file β-values with red color code) relative to the samples from patients with stable disease. A more detailed analysis of the differentially methylated CpG sites confirms this visual impression: The mean β-value (± standard error) of all 25 methylated CpG sites in the fibrotic progression group is 0.64 ± 0.02, versus 0.52 ± 0.01 in the stable disease group . Additional file β-value > 0.8) in the cohort showing fibrotic progression compared to the group with stable disease. Additional file The heat maps in Figs. In a next step, we investigated whether copy number gains or losses are able to identify patients undergoing fibrotic progress and whether a CNA analysis might support the DNA methylation classifier. Calculating gene copy number alterations from the genome wide DNA methylation data obtained with the EPIC array is a straightforward well established procedure .In Fig. In recent years, several groups , 8, 25 iASXL1, EZH2, IDH1/2, and, SRSF2, called “high molecular risk, HMR” [2-test, p = 0.0065. However, 7 out of 16 samples (44%) from patients displaying progress to myelofibrosis do not have any mutation in one of these five genes .In contrast to the methylation profile, the presence of well-described high molecular risk mutations in β-values of 6 CpG sites were used for an explorative multivariate analysis. The numbers of leukocytes and platelets were only available for 18 out of 33 patients and the concentration of hemoglobin only for 17 out of 33 patients. Therefore, these parameters were modeled independently from the other parameters using Bayesian logistic regression.For each patient, all available clinical parameters  as well as the LDA value calculated from the methylation p = 0.0346). However, the LD value calculated for each sample showed a better level of significance in this analysis (p = 0.0098) and also allows for accurate classification of samples with fibrotic progression but without rare CHIP mutations (which are frequent but not ubiquitous in samples with fibrotic progression).All demographic and clinical data including age, sex, leukocyte count, platelet count, and hemoglobin concentration did not show any statistically significant difference between the two cohorts. Only rare CHIP mutations Fig. b were siIt has already been speculated for a long time that DNA methylation aberrations accumulate over the life span of an organism due to the intrinsic error rates of the enzymatic machinery responsible for proper maintenance of these patterns  indicating an association between risk of progress to myelofibrosis and an increase in epigenetic age. Figure p = 0.003 Mann–Whitney-U test).Figure Analyzing histopathologically well characterized bone marrow trephines from prefibrotic PMF patients, we could show for the first time that the analysis of DNA methylation patterns is able to identify those patients who will progress to myelofibrosis. For this purpose, we employed the EPIC array technology which interrogates the methylation status of 850,000 individual CpG sites.In comparison with other hematological malignancies, surprisingly little is known about DNA methylation patterns in MPNs, their dynamics during course of disease, and their diagnostic and prognostic impact. In two recent comprehensive reviews about myeloproliferative diseases, aberrations in DNA methylation are not mentioned at all , 33.The review by Kim and Abdel-Wahab entitled “Focus on the epigenome in the myeloproliferative neoplasms”  concentrHpaII tiny fragment enriched by LM-PCR) that comprehensive DNA methylation profiling is able to distinguish the three BCR-ABL1-negative MPN subgroups polycythemia vera (n = 8), essential thrombocytosis (n = 6), and primary myelofibrosis (n = 11). Samples from pre-fibrotic PMF patients later showing progression to myelofibrosis were not analyzed at that time.Nischal et al.  demonstrThe findings from Nischal et al. were confirmed by Perez et al.  using thASXL1 mutations and distinct methylation profiles reported by Nielsen et al. [ASXL1 mutated case each. Also, DNMT3A and TET2 alterations are too rare in our cohort for robust meaningful conclusions. Only the previously reported association of mutations rarely affected by age-related clonal hematopoiesis (ARCH) with progression to fibrosis [ASXL1, EZH2, IDH1/2, or SRSF2 is not able to predict progression to myelofibrosis (p = 0.99).The interesting association between n et al.  could nofibrosis  could beTP53, incorporated by Grinfeld et al. in their recently published “MPN Personal Risk Score”[Also mutations in the tumor suppressor gene sk Score”, do not From the fibrotic progression subgroup 7 patients had a high molecular risk mutation Fig. , and 9 pIn a more recent genome-wide DNA methylation profiling project , Martinez-Calle et al.  identifiComparing DNA methylation profiles from healthy controls and patients with a certain disease or from patients with different diseases always comes with the risk of confounding factors distorting the DNA methylation profiles, the most important being cellular composition of the blood or the bone marrow, respectively , cases with two consecutive biopsies and a minimum of 1 year follow-up time  were selected. The first bone marrow biopsy at the time of initial diagnosis had to be classified as prefibrotic PMF without increase in blasts (EB0) according to WHO definition  and withThe cohort analyzed in this study represents a subgroup of the cohort described in detail in Bartels et al. 2020 . The selThe study design is following the guidelines of the Hannover Medical School ethics committee for retrospective analyses.Extraction of genomic DNA was performed using the Maxwell RSC instrument  and the Maxwell RSC DNA FFPE kit according to the manufacturer's instructions. Three to five sections of 10 µm thickness each were taken, depending on the size of the trephine. After extraction, nucleic acid concentrations were quantified using a Qubit 2.0 fluorimeter  and the Qubit dsDNA high sensitivity kit .β-value threshold. Gene set enrichment analysis (GSEA) against the Gene Ontology (GO) database was performed based on the differentially methylated regions (DMR) between both groups, which were identified using the Bumphunter algorithm included in the ChAMP package with an FDR of ≤ 0.05 and the parameters ‘maxGap’ and ‘cutoff’ changed from their default values to 250 and 0.99, respectively. The GSEA itself was performed using the gometh algorithm included in the ChAMP package and an FDR threshold of 0.1. The R package “conumee” in bioconductor was employed for identification of copy number variations [For genome-wide DNA methylation analyses, the 850 k EPIC array platform from Illumina was used . At leasriations . For theriations , 53.In order to select suitable DMPs for sample classification, we utilized an exact leaps and bounds algorithm aimed at optimizing the Tau-squared coefficient from the R package sub-select (version 0.14 ). The rep-value for the Z-statistic was below 0.05.In order to deal with the moderate sample sizes and occasional separation by chance, we used Bayesian logistic regression instead of logistic regression for our analysis of demographic and clinical data. Parameters were considered to be significant if their MPL Codon 515 and for ETNK1 Codon 244 as described [Targeted re-sequencing of 23 genes was performed using an amplicon-based NGS panel, and pyrosequencing was used for analysis of escribed .The analysis of copy number variations based on their methylation profiles was performed using the R package conumee (version 1.12.0) . For thiAdditional file 1: Figure S1. Singular value decomposition analysis for identification and exclusion of confounding factors. Sample age and amount of DNA both show a p-value above 0.05 in the SVD analysis, demonstrating that these two variables are clearly not confounding factors.Additional file 2: Figure S2. Hierarchical clustering based on 25 CpG sites from Fig. Additional file 3: Figure S3. Hierarchical clustering based on 1000 differentially methylated CpG sites and histogram of β-values. This figure corresponds to Fig. β-values illustrating the gain in methylation in the fibrotic progression cohort, accompanied also by a loss of DNA methylation in a smaller group of CpG sites.Additional file 4: Figure S4. Hierarchical clustering based on 6 CpG sites from Fig. Additional file 5: Table S1. Gene set enrichment analysis of differentially methylated regions against the Gene Ontology database.Additional file 6: Table S2. Detailed description of the 40 most differentially methylated regions (DMR).Additional file 7: Figure S5. Linear discriminant classification of low-quality samples. Three samples (white background labels) which had been excluded from the statistical analysis due to high proportions of probes above the detection p-value threshold of 0.1 are classified correctly using the linear discriminant model.Additional file 8: Figure S6. Histogram of all β-values with fixed thresholds for hyper- and hypomethylation. With a β-value threshold of > 0.8 for hypermethylation and < 0.1 for hypomethylation the simultaneous gain in hyper- and in hypomethylated loci in the fibrotic progression cohort is obvious. The number of CpG sites with very high methylation level  is larger in the FP cohort compared to the SD cohort  and this increase in highly methylated sites in the FP cohort is three times higher than the increase in CpG sites with very low methylation  in this cohort (relative to the SD cohort the number of CpG sites with low methylation level increases by only 7.7%). Therefore, it is justified to state that overall the fibrotic progression cohort is characterized by hypermethylation relative to the stable disease cohort.Additional file 9: Figure S7. Boxplots for the β-values of the 25 differentially methylated CpG sites shown in Fig. Additional file 10: Figure S8. Number of differentially methylated CpG sites in the FP group versus the SD group. a Number of CpG sites more heavily methylated (“hypermethylated”) in the FP or the SD group at a given threshold for the difference in the β-value. b Histogram for Δβ > 0.1. c Histogram for Δβ > 0.2. The predominance of more heavily methylated loci in the FP group is obvious. The SD group shows a distribution centered around β-values between 0.5 and 0.6, whereas the FP group shows a clear skewing of the distribution towards β-values between 0.8 and 0.9.Additional file 11: Figure S9. Singular value decomposition analysis of the batch correction. ComBat batch correction could be successfully used to reduce the influence of confounding variables (such as AMP_Plate) on the methylation data set, leaving “sample class” as the most important discriminator.Additional file 12: Table S3. Detailed description of the 25 most differentially methylated CpG sites at FDR < 0.1.