We also measured serum levels of NfL as a blood-based biomarker for axonal injury. Detailed methods are provided in Alzheimer’s disease (AD) is the most common cause of dementia. Although AD was initially considered to be a cell autonomous neurodegenerative disorder, marked neuroinflammation has been observed in brains of AD patients. Genetic and molecular biological findings have suggested the central nervous system (CNS) inflammatory processes to be involved in the etiopathogenesis of AD, in which the activated microglia play a key role. This has also been supported by epidemiological observation that CNS infections are associated with the development of AD, and the relationship between herpes simplex virus (HSV)-1 and AD has been particularly well investigated . Anti-he1–42, Aβ1–40, and the Aβ1–42/Aβ1–40 ratio were significantly lower in the HSV + VZV combined group (HSV/VZV) compared with the control group   . Among the biomarkers, only CSF NfL was significantly correlated with the Modified Rankin Scale on discharge after age adjustment (p = 0.018). Supplementary Fig. Results of uni- and multivariate regression analyses between those biomarker values and clinical severity are summarized in Supplementary Table 1–42, and increased t-tau and p-tau in CSF might be shared not only by CNS involvement of HSV infection but also by CNS VZV infection. This combination of biomarker changes, the so-called “AD signature”, has been considered to indicate the presence of AD pathology. In particular, high levels of p-tau at threonine181 have been reported to occur solely in AD and not in other neurodegenerative disorders or acute brain damage, such as acute brain infarction [The current study has three major implications. First, to our best knowledge, this is the first report of CSF p-tau elevation in patients with CNS VZV infection. This suggests that the biomarker profile of decreased Aβfarction . The CSFfarction , as simiSecond, the elevations of CSF sTREM2 and GFAP in the HSV and VZV groups are in line with previous observations . These tThird, only the NfL levels were significantly correlated with the severity and a poor outcome after age adjustment in the CSF biomarkers. This suggests that the NfL concentration in CSF obtained for the diagnostic purpose on admission is the most powerful predictive marker for the severity and prognosis in patients with herpes virus infections among the molecules tested in this study. The CSF and blood NfL levels are reported to be tightly correlated in neurological disorders. Here we found that the serum levels of NfL were strongly associated with their matching CSF levels . Figure S2. Scatter plots of the biomarkers vs the lowest score of GCS during hospitalization  as well as the biomarkers vs mRS score at discharge  in the HSV/VZV group. Figure S3. Correlations between CSF and serum NfL levels. There was a strong positive correlation between CSF and serum NfL levels by Spearman’s rank correlation test (p < 0.0001).


We report that, at this developmental stage, the ChP showed increased ATP production and increased expression of ATP-dependent K+ transporters, particularly the Na+, K+, Cl−, and water cotransporter NKCC1. Overexpression of NKCC1 in the ChP resulted in increased CSF K+ clearance, increased cerebral compliance, and reduced circulating CSF in the brain without changes in intracranial pressure in mice. Moreover, ChP-specific NKCC1 overexpression in an obstructive hydrocephalus mouse model resulted in reduced ventriculomegaly. Collectively, our results implicate NKCC1 in regulating CSF K+ clearance through the ChP in the critical period during postnatal neurodevelopment in mice.Cerebrospinal fluid (CSF) provides vital support for the brain. Abnormal CSF accumulation, such as hydrocephalus, can negatively affect perinatal neurodevelopment. The mechanisms regulating CSF clearance during the postnatal critical period are unclear. Here, we show that CSF K + and fluid volume through the choroid plexus during postnatal development in mice.Abnormal CSF accumulation in the brain can lead to hydrocephalus. The mechanisms regulating CSF clearance during early development are unclear. Here, the authors show that NKCC1 regulates the clearance of both CSF K Disrupted CSF volume homeostasis with excessive CSF accumulation is implicated in pediatric brain disorders, in particular congenital hydrocephalus2, where patients suffer from a potentially life-threatening accumulation of CSF and frequently develop neurological deficits that last through childhood and into adult life3. Often schizophrenia patients have enlarged lateral ventricles by their first episode of psychosis4, in some cases as early as infancy5, suggesting a role for CSF clearance abnormalities in this and possibly other neurodevelopmental disorders. As another example, autism spectrum disorders are associated with altered CSF distribution patterns and enlarged CSF space surrounding the brain6. A better understanding of developing CSF dynamics may help explain why early phases of brain development  represent a period of high vulnerability to certain congenital disorders7.A balance between cerebrospinal fluid (CSF) production and clearance (influx/efflux) is essential for normal brain function and development9. While many mechanistic questions and validation work remain regarding the identified routes10, their developmental time-courses are even less understood. In human, arachnoid granulations are not fully formed until 2 years of age. In other mammalian species , lymphatic vessels contribute to CSF clearance by connecting with CSF spaces around the cribriform plate and olfactory nerve roots during perinatal brain development13. Similarly, lymphatic structures begin to sprout at the base of the skull around birth in mice, but mature throughout the first postnatal month14. Thus, available data suggest that the abovementioned CSF clearance routes are unlikely to fully account for CSF outflow during the critical and sensitive early phase of brain development. Identifying and manipulating the early endogenous CSF clearance mechanisms could provide one powerful approach for tackling neurodevelopmental disorders involving CSF dysregulation, and may also be applied to fluid disorders affecting adults.Critically, how CSF is cleared during this perinatal period remains a mystery. Progress in CSF dynamics research has identified several putative CSF clearance routes including arachnoid villi and granulations in human , perineural and paravascular pathways, and meningeal lymphatics15. Although the prevailing model posits that the ChP provides net luminal secretion of ions and water to form CSF in adult brains18, historical clinical observations suggest some absorptive functions of the ChP19, which is supported by animal studies20. Moreover, extensive studies have documented ion gradients driving bidirectional trafficking at the ChP-CSF interface through various ChP transporters22. Furthermore, broad transcriptional changes of the machinery regulating fluid/ion transport support the concept of temporally dynamic and possibly context-dependent ChP functions in determining net directionality of CSF transport24.To identify how early CSF is cleared, we investigated tissues with the ability to modulate CSF at this stage. The choroid plexus (ChP) is an intraventricular epithelial structure that forms the majority of the blood-CSF barrier and emerges prenatally. It contains diverse ion and fluid transporters along its vast surface area+-K+-Cl− and water cotransporter, NKCC1, on the apical membrane of the ChP during a specific stage. While NKCC1 retains bidirectional transporter potential throughout life, its role in mediating net CSF-to-ChP transport during the early postnatal period of brain development critically influenced the establishment of CSF ion and fluid homeostasis. These results have implications for the pathophysiology of congenital disorders accompanied by dysregulated CSF and could inform strategies for treatment of neonatal hydrocephalus and perhaps other disorders.To further explore potentially absorptive properties of the early ChP, we studied the expression of transporters, the energy systems and ionic gradients that govern their activity, and their physiological effects across the timespan of early postnatal development in mice. Taken together our data support a previously undescribed developmental mechanism for net CSF clearance by the Na+] decreased rapidly. We used inductively coupled plasma–optical emission spectrometry (ICP-OES) and ion chromatography (IC) to measure levels of key ions likely to govern CSF flux including Na+, K+, and Cl− at several developmental timepoints. CSF [K+] was remarkably high at birth (9.6 ± 3.5 mM), decreased rapidly to 4.4 ± 0.9 mM by P7 . Notably, the CSF-to-serum [K+] ratio decreased sharply from P0 to P7  , postnatal day 0 (P0), P7, and adult mice. We then calculated basal metabolism and ATP production  Fig. , while cion Fig. . In addi+ clearance and high ChP metabolism, we found that expression of energy-dependent cation transport pathway components was upregulated in ChP postnatally. To identify which of the ChP transporters are likely candidates controlling postnatal CSF clearance through the ChP, we conducted ribosomal profiling to investigate transcripts that are prioritized for translation in embryonic (E16.5) and adult ChP, using Translating Ribosomal Affinity Purification (TRAP38). ChP epithelial cells were targeted by crossing FoxJ1:cre mice39 with TRAP (EGFP:L10a) mice38  between E16.5 and adult ChP: 1119 enriched at E16.5 and 847 enriched in adults  as the candidate most likely to mediate CSF clearance. NKCC1 is functionally related to Na+/K+-ATPase (Atp1a1 and Atp1b1), as the latter actively maintains the Na+/K+ gradient that partially drives NKCC1 directionality. Both the Na+/K+-ATPase and NKCC1 are capable of CSF K+ clearance, but NKCC1 was of particular interest because (1) it is a cotransporter of K+ and water40; and (2) the activity of NKCC1 can be further modified by phosphorylation41, lending additional control over its fluid/ion modulatory capacity. In addition, NKCC1 is particularly enriched in the ChP and does not impact broad functionality like the Na+/K+-ATPase does, both of which are important features for a functional therapeutic intervention target. We refined our temporal expression analyses of NKCC1, ATP1a1, ATP1b1, and Klotho (Kl), which contributes to the membrane localization of the Na+/K+-ATPase43  in the ChP of P0-P7 pups, with P7 having peak pNKCC1 levels among all postnatal ages, indicative of increased NKCC1 activity during the first postnatal week , the histone deacetylases HDAC1/2, and methyl CpG-binding domain protein MBD3 in the ChP 47 using an adeno-associated viral vector (AAV) with tropism for the ChP (AAV2/5)48, delivered by in utero intracerebroventricular (ICV) injection at E14.5. Chd4 transcript levels decreased to <50% by P7  in developing ChP epithelial cells using AAV2/5. NKCC1 transport directionality follows the combined Na+, K+, Cl− gradients, which are close to neutral in adult brains and likely to bias toward the CSF-to-ChP direction during the early postnatal phase. NKCC1 protein level would be rate-limiting during the early postnatal time when it is already highly phosphorylated, unlike in older mice where pNKCC1 only represented a small portion of total NKCC1. The goal of this OE approach was to accelerate endogenous ChP NKCC1 transport, thereby revealing its directionality based on whether CSF [K+] clearance was enhanced or delayed. AAV2/5-NKCC1, which expresses NKCC1 fused to an HA tag49, or control GFP virus was delivered by in utero ICV at E14.5. Successful NKCC1 OE and increased pNKCC1 was confirmed in ChP at P0 . Overall, these findings support a model in which, under physiological conditions with high early postnatal CSF [K+], ChP NKCC1 transports K+ out of the CSF.To test whether NKCC1 is indeed capable of transporting from CSF into the ChP during the period of rapid CSF  is above adult values, especially during the first postnatal week in mice. Future studies should shed light on how this critical period corresponds to the human developmental timeframe, and whether shared mechanisms during development underlie vulnerability to disorders involving CSF.NKCC1 is a bidirectional transporter, recently shown to be an important cotransporter of water in the adult ChPon); and  accurate60. Here, enhancing ChP epithelial cell NKCC1 transport capacity lessened the severity of ventriculomegaly in a model of obstructive hydrocephalus. Our data suggest the possibility of treating congenital hydrocephalus by augmenting endogenous ChP NKCC1 activity to increase CSF absorption rates during early development when CSF [K+] is high. In adult animals, CSF K+ is rapidly removed when the concentration exceeds homeostatic levels29. It is tempting to speculate that depending on circumstances, ChP NKCC1 may provide a route for CSF K+ clearance in adults as well.We next demonstrated that ChP clearance of CSF can be targeted to temper abnormal CSF accumulation. CSF adjacent tissues, including ChP, have been targeted for therapeutic manipulation in rodent models of neurologic diseases ranging from Huntington’s disease and lysosomal storage disorders, to Alzheimer’s disease, where transduction of exogenous gene products has improved cardinal features of disease46, we found that the CHD4/NuRD complex is required for developmental regulation of NKCC1 expression. This connection suggests a possible pathophysiological mechanism whereby lack of CHD4 activity may reduce NKCC1 levels during early development , and lead to insufficient CSF clearance resulting in hydrocephalus. In our loxP-cre approach, most CHD4 protein knockdown and resulting attenuation of NKCC1 expression occurred by P14, which is beyond the critical window of high NKCC1 activity observed between P0-P7. Improved genetic tools for even earlier CHD4 knockout and new animal models harboring the de novo patient mutations would be required to fully unravel the regulatory connection between the CHD4/NuRD complex and NKCC1.In light of recent findings reporting hydrocephalus and ventriculomegaly in children with de novo loss-of-function CHD4 mutations61. Consistent with progenitor involvement in CSF dynamics, recently identified genes driving pediatric hydrocephalus are expressed predominantly by cortical progenitor cells62, and not by the ChP.A key question that emerges from this work is: If ChP NKCC1 mediates net CSF clearance during this transitional developmental stage, what other sources contribute to the influx of early CSF water and ions? In addition to possible ChP water secretion that is independent of NKCC1, forebrain progenitor cells that have a cell body at the ventricular surface and extend their basal processes to the developing pia could play a role in early CSF generation+ during the first postnatal week. Ionic homeostasis of the CSF plays critical roles in brain development and function. The enzyme that moves K+ against its individual concentration gradient, the Na+/K+-ATPase , is not yet expressed at adult levels during this period. The ChP NKCC1-mediated K+ clearance mechanism might assist in establishing the K+ gradient in a timely manner, which is crucial for cellular physiology63. Notably, the period of rapidly decreasing CSF [K+] overlaps with the developmental phase when the excitatory-to-inhibitory “GABA switch” occurs. In early cortical progenitor cells that reside in the ventricular zone and are bathed by CSF, the classic inhibitory neurotransmitter GABA leads to excitatory potentials and suppression of DNA synthesis64. As newborn cortical neurons differentiate and migrate away from the ventricular zone, GABA adopts its classic role as an inhibitory neurotransmitter in the context of lower intracellular Cl− 65, which is achieved through coordinated activities of neuronal K+/Cl− co-transporters KCC2 and NKCC167. Because ions can traffic from CSF into interstitial fluid69, any interference with the developmental timeline of ChP NKCC1 function that results in delayed CSF K+ clearance could potentially increase extracellular/interstitial fluid [K+] and affect global neural excitability63. Specifically, such a change in extracellular/interstitial fluid [K+] could fundamentally impact neuronal NKCC1 and KCC2 transport equilibrium, potentially contributing to a delayed GABA switch, a phenomenon reported in many models of neurodevelopmental and psychiatric disorders including subtypes of autism spectrum disorder70, Rett syndrome71, Fragile X syndrome72, schizophrenia73, and Down syndrome74. Furthermore, extracellular [K+] and certain K+ channels also regulate microglia75, which are critical for synaptic pruning during postnatal development in mice76. Thus, the ChP is poised to coordinate proper CNS formation by maintaining an optimal extracellular environment at different developmental stages, with possible effects on neuronal maturation, circuit formation, and neuroimmune interactions. Similarly, since many experimental paradigms in neurobiology commonly utilize aCSF to mimic the extracellular fluid environment, it is important to note that CSF ionic composition changes substantially over the course of development. In the present study, we provide ionically adjusted aCSF recipes with Na+, K+, Ca2+, Mg2+, and Cl− values for multiple developmental stages including embryonic, neonatal, postnatal, and adult.In addition to fluid regulation, our newly identified ChP clearance route provides a means for regulating extracellular K14, leptomeningeal vasculature77, and the ependyma78. While this approach measures overall cranial fluid dynamics as one single unit, future applications could apply mathematical models that have been proposed to isolate the contribution of distinct CSF outflow routes using data acquired from patients79.Beyond key findings and implications during this critical developmental stage, our study introduced a murine ICP measurement device combined with constant CSF infusion. This approach can be broadly applied to study the CSF system across the mouse lifespan. We adapted our tool from clinical practice to provide a range of options for measuring global cerebral fluid states that reflect the interaction between CSF and cranial tissues. In later life, CSF homeostasis is maintained by collaborative efforts from multiple putative players in the brain, including the ChP, the dural lymphaticsOur findings reveal that the ChP regulates CSF fluid volume and ion composition at an early stage, which may play a role in guiding early brain development. Targeting this absorption route holds promise for improving fluid management in congenital hydrocephalus and other brain disorders.The Boston Children’s Hospital IACUC approved all experiments involving mice in this study. Timed pregnant CD1 dams were obtained from Charles River Laboratories. Mice with germline loxP-CHD4-loxP were imported from MGH and bred in-house. Both male and female mice were equally included in the study and were analyzed at postnatal day 0, 7, 14, 21, 28, 5–7 weeks, and 2+ months. Animals were housed in a temperature and humidity controlled room on a 12-h light/12-h dark cycle and had free access to food and water. All mice younger than postnatal day 10 were allocated into gestational/postnatal age groups without respect to sex . We intentionally included both males and females in studies involving mice older than 10 days.g for 10 min at 4 °C to remove any tissue debris. Metal quantification was performed by Galbraith Laboratories, Inc . Inductively coupled plasma–optical emission spectrometry (ICP-OES) was used for K+ and Na+ quantification, and ion chromatography (IC) was used for the Cl− quantification. All tests were performed using 5–10 μL of CSF. The quality control (QC) recovered at 94.4% for K+, 97.7% for Na+, 100.1% for Cl−, 95.1–118% for Mg2+, and 107.5 % for Ca2+.CSF was collected by inserting a glass capillary into cisterna magna, and collected CSF was centrifuged at 10,000 × Foxj1:Cre × EGFP-L10a Bacterial Artificial Chromosome (BAC) transgenic lines (N = 3 samples of LVChP pooled from 3- to 8-week-old mice or 3 litters of E16.5 embryos). Brain tissue was immediately dissected and used for TRAP RNA purifications38. ChP tissues were dissected in ice-cold dissection buffer  and homogenized in lysis buffer  at 900 rpm for 12 strokes on ice with Teflon pestles in glass tubes (Kontes). Post-nuclear supernatant was prepared by centrifugation at 4 °C for 10 min at 2000 × g. Post-mitochondrial supernatant was prepared after incubation with additional 1% NP-40 and 30 mM DHPC, followed by centrifugation at 4 °C for 10 min at 20,000 × g. Immunoprecipitation was performed with magnetic streptavidin beads  conjugated to biontinylated anti-GFP antibodies (clones 19C8 and 19F7) for 16 h at 4 °C with gentle end-over-end rotation. Beads were collected on a magnet on ice, washed four times with 1000 μL 0.35 M KCl wash buffer . RNA was eluted in Stratagene Absolutely RNA Nanoprep Kit (Agilent #400753) lysis buffer (with β-ME) and purified according to kit instructions. RNA quality was assessed using Bioanalyzer Pico Chips  and quantified using Quant-iT RiboGreen RNA assay kit (Thermo Fisher Scientific R11490). Libraries were prepared using Clonetech SMARTer Pico with ribodepletion and Illumina HiSeq to 50NT single end reads. Sequencing was performed at the MIT BioMicroCenter.We used a total of 9 mice aged 8 weeks and 9 litters of mouse embryos aged E16.5 from the 80. The mapped reads were processed by htseq-count of HTSeq software (v 0.6.0)81 with mm10 gene annotation to count the number of reads mapped to each gene. The Cuffquant module of the Cufflinks software (v 2.2.1)82 was used to calculate gene FPKM (Fragments Per Kilobase of transcript per Million mapped reads) values. Gene differential expression test between different animal groups was performed using DESeq2 package (v. 1.26.0)83 with the assumption of negative binomial distribution for RNA-Seq data. Genes with adjusted p-value < 0.05 are chosen as differentially expressed genes. All analyses were performed using genes with FPKM > 1, which we considered as the threshold of expression.The raw fastq data of 50-bp single-end sequencing reads were aligned to the mouse mm10 reference genome using STAR RNA-Seq aligner (v2.4.0j)84. Gene ontology (GO) analysis was performed using AdvaitaBio iPathway guide v.v1702. Enrichment vs. perturbation analysis was performed by AdvaitaBio iPathway guide v.v1702 and allows comparison of pathway output perturbation and cumulative geneset expression changes. In brief, the enrichment analysis is a straightforward geneset enrichment over-representation analysis (ORA) considering the number of differentially expressed genes (DEGs) that are assigned to a given pathway. The enrichment value is expressed as a proportion of enriched members to total genes in a defined pathway and a p-value (Fisher) is calculated for this score; however, false positives have been reported at up to 10% with this method85. Perturbation, on the other hand, uses pathway data that applies relationships between gene products rather than only using a list. Perturbation assigns an impact score based on a mathematical model that captures the entire topology of the pathway and uses it to calculate how changes in the expression of each gene in the pathway would perturb the absolute output of the pathway85. Then, these gene perturbations are combined into a total perturbation for the entire pathway and a p-value is calculated by comparing the observed value with what is expected by chance. Motif analyses were performed using SignalP (v5.086) and TMHMM (v2.087).Functional annotation clustering was performed using DAVID v6.76) for 1 h, washed in water three times and incubated in 1% aqueous uranyl acetate for one hour. This was followed by two washes in water and subsequent dehydration in grades of alcohol . Samples were then incubated in propyleneoxide for 1 h and infiltrated overnight in a 1:1 mixture of propyleneoxide and TAAB Epon . The following day, the samples were embedded in TAAB Epon and polymerized at 60 degrees C for 48 h. Ultrathin sections (about 80 nm) were cut on a Reichert Ultracut-S microtome, and picked up onto copper grids stained with lead citrate. Sections were examined in a JEOL 1200EX Transmission electron microscope or a TecnaiG² Spirit BioTWIN. Images were recorded with an AMT 2k CCD camera.All tissue processing, sectioning, and imaging was carried out at the Conventional Electron Microscopy Facility at Harvard Medical School. Forebrain tissues were fixed in 2.5% glutaraldehyde/2% paraformaldehyde in 0.1 M sodium cacodylate buffer (pH 7.4). They were then washed in 0.1 M cacodylate buffer and postfixed with 1% osmiumtetroxide (OsO4)/1.5% potassium ferrocyanide . From each animal, 10–20 fields of view were imaged at 3000× for glycogen analysis and 5–10 fields of view were imaged at 3000× for mitochondrial analysis. Each different field of view represented a unique cell or cells, and fields of view were chosen such that both the apical and basal surfaces of the cell were visible. For mitochondrial distribution, a custom MatLab (v.2018) code was written to extract the centroid from mitochondria data traced in ImageJ ROIs . Then a distance transformation was performed from each mitochondrion centroid to the hand-traced apical or basal surfaces. The shortest distance was extracted to calculate the apical: basal proximity ratio, such that 1 = on the apical surface and 0 = on the basal surface. The analyses included a total of 1747 adult mitochondria, 2241 P7 mitochondria, 2257 P0 mitochondria, and 1123 embryonic mitochondria. The same groups of embryonic and adult animals were used for the above analysis.Glycogen and mitochondrial quantification was performed by hand using the ImageJ plugin FIJI2 incubator. Extracellular acidification rates (ECAR) and oxygen consumption rates (OCR) were measured via the Cell Mito Stress Test  with a Seahorse XFe96Analyzer (Agilent) following the manufacturer’s protocols. Data were processed using Wave software (Agilent). ATP production was calculated as the difference in OCR measurements before and after oligomycin injection, as described by the manufacturer’s protocol . The Cell Mito Stress test was performed 2–5 independent times . The individual analyses were performed by averaging the readings from both the right and left hemisphere lateral ventricle ChP for each individual. Data were normalized by Calcein-AM  fluorescence measured at the end of the assay. Each datum is the normalized average of the 2 LVChP from each individual normalized to the average of the adult levels run on the same plate for each assay to account for any experimental variability. Raw and processed files are included in source data.ChP explants were dissected in HBSS  and maintained on wet ice until plated. Only the posterior leaflet of the P0, P7, and adult ChP was retained for analysis due to empirically determined limitations of the oxygen availability in the XFe96 Agilent Seahorse system. Tissue explants were plated on Seahorse XFe96 spheroid microplates  coated with Cell TAK (Corning), in Seahorse XF Base Medium  supplemented with 0.18% glucose, 1 mM L-glutamine, and 1 mM pyruvate at pH 7.4 and incubated for 1 h at 37 °C in a non-CO°C artificial CSF . Protein was not added to aCSF because, while in rat, the protein concentration decreases 10× between P2 and adult90; this change is only 2× in mouse45 and unlikely to contribute substantially to the driving force. The tissues were soaked in 1.8 ml aCSF at the beginning of each imaging session and allowed to stabilize for 10 min. One Z-stack was acquired to reflect the baseline cell volume. Then a 10× KCl solution in aCSF was spiked into the bath to bring the final bath K+ concentration to 50 mM immediately before imaging continued. A total of five 3D Z-stacks were acquired throughout a 10-min imaging session to capture changes in cellular volume over time. Each stack acquisition took less than 30 s to minimize changes in cell volume from the beginning to the end of each stack. All imaging studies were carried out at 37 °C. Image stacks were imported into Imaris  software. Individual epithelial cells were identified by shape. Cells with discrete borders that were present at all timepoints and had dark pixels both above and below them in Z for the whole timecourse were selected a priori and then traced by hand using the “Surpass” functionality to create a 3D surface volume through all Z-stacks based on Calcein-AM uptake signal. Due to known z-step distance and interpolation between the planes, Imaris calculated the number of voxels for each cell. This analysis was then repeated for the same cell throughout the timecourse. We verified manually that the same cell was analyzed throughout the timecourse based on the topology of the surrounding cells, allowing for adjustment for any x–y drifting that occurred. The relative volume was calculated as dV/V0 for each timepoint (t) where V0 is the initial volume of the cell, t is each subsequent timepoint after addition of challenge, and dV = Vt − V0. In LVChP from each animal, 5 distinct cells were analyzed and their values at each timepoint were averaged to represent the average value for each animal. A total of 4 animals (N = 4) were analyzed from each age group.Fresh LVChPs were dissected from P4 pups and adult mice in room temperature HBSS and glued down onto imaging dishes with coverslip bottom. The tissues were incubated at 37 °C with Calcein-AM  for 10 min and then rinsed with 37 Samples were fixed in 4% paraformaldehyde (PFA). For cryosectioning, samples were incubated in the following series of solutions: 10% sucrose, 20% sucrose, 30% sucrose, 1:1 mixture of 30% sucrose and OCT (overnight), and OCT (1 h). Samples were frozen in OCT.Cryosections were blocked and permeabilized , incubated in primary antibodies overnight and secondary antibodies for 2 h. Sections were counterstained with Hoechst 33342  and mounted using Fluoromount-G (SouthernBiotech). The following primary antibodies were used: chicken anti-GFP , mouse anti-Aqp1 , rabbit anti-CHD4 , rabbit anti-NKCC1 , rat anti-HA . Secondary antibodies were selected from the Alexa series . Images were acquired using Zeiss LSM880 confocal microscope with ×20 objective and ×63 oil objective. ZEN Black software was used for image acquisition and ZEN Blue used for Airy processing.Tissues were homogenized in NET buffer  supplemented with protease inhibitors. Protein concentration was determined by BCA assay (Thermo Scientific 23227). Lysates with same amount of total protein (250–1000 µg based on experiments) were precleared at 4 °C for 2 hr with Protein G agarose and then incubated with desired antibody or control antibody at 4 °C overnight (no beads present during antibody incubation). Protein G agarose beads were added to lysate-antibody mixture after overnight incubation for 2 h. Beads were washed thoroughly and then eluted by boiling in 2% SDS. ChP tissues were pooled across seven litters of P0 pups and 30 adults to achieve sufficient protein for Co-IP.Tissues were homogenized in RIPA buffer supplemented with protease and phosphatase inhibitors. Protein concentration was determined by BCA assay (Thermo Scientific 23227). Samples were denatured in 2% SDS with 2-mercaptoethanol by heating at 37 °C (for NKCC1) or 95 °C (for CHD4 and other NuRD complex proteins) for 5 min. Equal amounts of proteins were loaded and separated by electrophoresis in a 4–15% gradient polyacrylamide gel (BioRad #1653320) or NuPAGE 4–12% Bis-Tris gel (Invitrogen #NP0322), transferred to a nitrocellulose membrane , blocked in filtered 5% BSA or milk in TBST, incubated with primary antibodies overnight at 4 °C followed by HRP conjugated secondary antibodies (1:5000) for 1 h, and visualized with ECL substrate. For phosphorylated protein analysis, the phosphoproteins were probed first, and then blots were stripped (Thermo Scientific 21059) and reprobed for total proteins. For co-IP protein analysis, TrueBlot secondary antibody (eBioscience 18-8816-33) was used to detect only nondenatured IgG and avoid background signal from IP antibody. The following primary antibodies were used: rabbit anti-NKCC1 , rabbit anti-pNKCC1 , rabbit anti-ATP1a1 , goat anti-Klotho , rabbit anti-GAPDH , mouse anti-HA , rabbit anti-CHD4 , rabbit anti-MBD3 , rabbit anti-HDAC1 , mouse anti-HDAC2 . The same protein samples were blotted for multiple targets to allow controlled quantitative comparison . Raw blot images are included in Source Data.−ΔΔCT method. Technical replicates were averaged for their cycling thresholds and further calculations were performed with those means. Each RNA sample was analyzed for multiple genes.For mRNA expression analyses, the ChP were dissected and pooled from 2 pups. RNA was isolated using the MirVana miRNA isolation kit (Invitrogen AM1561) following manufacturer’s specifications without miRNA enrichment step. Extracted RNA was quantified spectrophotometrically and 100 ng was reverse-transcribed into cDNA using the High Capacity cDNA Reverse Transcription kit (Applied Biosystems #4368814) following manufacturer’s specifications. RT-qPCRs were performed in duplicate using Taqman Gene Expression Assays and Taqman Gene Expression Master Mix (Applied Biosystems) with GAPDH as an internal control. Cycling was executed using the StepOnePlus Real-Time PCR System (Invitrogen) and analysis of relative gene expression was performed using the 2Timed pregnant mice (E14.5) were anesthetized with isoflurane and warmed. Laparotomy was performed. Embryos were stabilized by hand and lateral ventricles were visually identified. AAV solution was delivered into lateral ventricles using fine glass capillary pipettes with beveled tips. The AAV solution contained (0.01%) Fast Green FCF (Sigma F7252-5G) to aid with visualization. Uterine horns were returned into the abdominal space, and the incision was sutured. Meloxicam analgesia was longitudinally delivered according to IACUC protocol.TM ID 70200742529). The pups were warmed and returned to the dam.Postnatal day 4 pups (P4) were anesthetized. A small incision was made in the scalp, and 1 µl of sterile kaolin solution (25% in PBS) was injected into the left lateral ventricle using glass capillary pipettes. The injection site with corresponding lateral ventricle localization was determined as between bregma and lambda, and 1 mm from mid-line. The skin incision was sutured and reinforced with Vetbond . The plasmid carries an 3×HA tag at the N-terminal of NKCC1 to allow detection and separation from endogenous NKCC1. The CFP tag was removed by BsaI digestion to reduce insert size for AAV production. Virus production and purification were performed by the Penn Vector Core. The very large size of the plasmid resulted in variable transduction efficiency. All mice receiving AAV-NKCC1 were analyzed for HA expression after every experiment to confirm transduction efficiency. AAV-GFP and AAV-Cre were purchased from the Boston Children’s Hospital Viral Core.The original AAV-NKCC1 plasmid was purchased from Addgene (pcDNA3.1 HA CFP hNKCC1 WT (NT15-H) was a gift from Biff Forbush: Addgene plasmid # 49077; 91 and exported through ParaView.Mice were imaged using Bruker BioSpec small animal MRI (7T) at 2 week and P50 while under anesthesia by isoflurane. A warm pad was used to maintain body temperature. Breathing rate and heart rate were monitored to reflect the depth of anesthesia. All axial T2 images were acquired using the following criteria: TE/TR = 60/4000; Ave = 8; RARE = 4; slice thickness = 0.6 mm. Ventricle volumes were calculated by manual segmentation using FIJI/ImageJ. Brain sizes were calculated by averaging total cross-sectional areas from the four highlighted slices (red) with visible LV and 3 V in control mice (corresponding slices were used in AAV-NKCC1 regardless of visibility of ventricles). Ventricle regions were not excluded from the brain size measurements. In studies with unilateral kaolin injection, 3D reconstruction of the ventricles was performed by manual segmentation in ITK-SNAP54infusion is the rate of infusion, ICPbaseline is the ICP level before infusion, p0 is a pressure in the storage arm of the model, RCSF is the resistance to CSF outflow, and Ci is the compliance coefficient.An apparatus was developed to perform a constant infusion test in mice through a single catheter for both infusion of CSF and monitoring of ICP. A 20-cc syringe was filled with aCSF and placed in an automated infusion pump  set to deliver a constant rate infusion of 1–4 uL/min. The syringe was connected via pressure tubing to hemostasis valve Y connector . A fiberoptic ICP sensor  was inserted through the other port of the rotating hemostat and then into 0.55 mm diameter catheter until the sensor was flush with the catheter’s distal tip. The entire apparatus and tubing was carefully screened to ensure the absence of air bubbles. Adult mice were then deeply anesthetized, placed on a warm pad, and head-fixed with ear bars. The distal end of the infusion device (catheter with fiberoptic sensor) was placed inside the lateral ventricle through a hole made by a hand-held twist drill with a #74 wire gauge bit (−0.4 mm (anterior-posterior) and 1.2 mm  with respect to Bregma, and a depth of 2 mm from the outer edge of the skull); the catheter was then sealed with Vetbond . Intraventricular access and water-tight seal was confirmed by observation of arterial and respiratory waveforms in the ICP signal and a transient rise in ICP upon gentle compression of the abdomen and thorax. Two min of baseline ICP were recorded before initiating the infusion of aCSF. As the infusion proceeded, careful observation was made of the mouse’s respiratory rate. After the ICP level reached a new plateau, the infusion was discontinued. Parameters of the Marmarou model of CSF dynamics for constant rate infusions were estimated by a non-linear least squares fit of the model to the ICP dataN) were defined as samples from distinct and biologically independent individual animals, analyzed either in the same experiment or within multiple experiments, with the exception when individual animal could not provide sufficient sample , in which case multiple animals were pooled into one distinct biological replicate and most details are stated in the corresponding figure legends. Due to limited space, these metrics for Fig. N = 6 biological replicates. Figure N = 3 distinct animals from two independent experiments , 5–10 field of view (FOV) per animal (See source data for exact numbers of FOV), distinct cells were captured in each FOV. Figure 1h, iN = 26 E16.5 embryos (4 litters); N = 19 P0 pups (2 litters); N = 12 P7 pups (2 litters); N = 16 adults. Data are from five independent experiments for adult; two independent experiments for P7 and P0; three independent experiments for E16.5. Each datapoint is an average of the 2 LVChP from an individual. j–k N = 3 animals per age; 433–833 mitochondria per individual.Biological replicates (CSF). In these cases the same image, protein or RNA samples, and mice were analyzed for multiple readouts and targets, to allow controlled quantitative comparison. Statistical analyses were performed using Prism 7 or R. Outliers were excluded using ROUT method (Q = 1%). Appropriate statistical tests were selected based on the distribution of data, homogeneity of variances, and sample size. The majority of the analyses were done using one-way ANOVA with multiple comparison correction (Sidak) or Welch’s two-tailed unpaired t-test, except for Fig. p values < 0.05 were considered significant . Exact p values can be found in the figure legends. P values are also marked in the figures where space allows.The following measurements were taken using the same set of samples/animals: mitochondria/glycogen quantification, immunoblotting, qPCR, and different parameters from CSF infusion tests (ICP, compliance, RFurther information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Reporting Summary


The molecular characterisation of medulloblastoma, the most common paediatric brain tumour, is crucial for the correct management and treatment of this heterogenous disease. However, insufficient tissue sample, the presence of tumour heterogeneity, or disseminated disease can challenge its diagnosis and monitoring. Here, we report that the cerebrospinal fluid (CSF) circulating tumour DNA (ctDNA) recapitulates the genomic alterations of the tumour and facilitates subgrouping and risk stratification, providing valuable information about diagnosis and prognosis. CSF ctDNA also characterises the intra-tumour genomic heterogeneity identifying small subclones. ctDNA is abundant in the CSF but barely present in plasma and longitudinal analysis of CSF ctDNA allows the study of minimal residual disease, genomic evolution and the characterisation of tumours at recurrence. Ultimately, CSF ctDNA analysis could facilitate the clinical management of medulloblastoma patients and help the design of tailored therapeutic strategies, increasing treatment efficacy while reducing excessive treatment to prevent long-term secondary effects. Non-invasive and precise methods are critical for monitoring paediatric brain cancers. Here the authors show that the molecular alterations and heterogeneity of paediatric medulloblastomas can be reliably detected in circulating tumour DNA from the cerebrospinal fluid – a routinely collected sample. This embryonal tumour of the central nervous system (CNS) is a complex and evolving heterogeneous disease with a wide range of prognosis being in some cases an extremely aggressive malignancy. MB can be subdivided into four molecular consensus subgroups: WNT, SHH, group 3 and group 4. Further subtypes have been proposedTreatment for MB includes tumour resection, chemotherapy and, for most patients, craniospinal radiotherapy. Brain tumour surgical resection is currently the first treatment option and surgical specimens are used for diagnosis and tumour characterisation. However, lack of sufficient sample, tissue artifacts or excessive presence of non-tumour tissue can create uncertainty to accurately characterise and classify tumours.7.In addition to the interpatient tumour heterogeneity, intratumour heterogeneity has been described in MB and the efficacy of single biopsies to representatively characterise this complexity can be limited. The fact that spatial heterogeneity has been reported at the level of somatic mutations and copy number alterations (CNAs) exemplifies the limitations of single biopsies9. The comparison of primary and relapsed tumours has shown that specific molecular alterations can appear in the relapse setting11. In addition, secondary radiotherapy-induced tumours can emerge in some patients14. To assess metastatic dissemination, cerebrospinal fluid (CSF) cytology together with brain and spinal MRI are performed and patients are classified according to Chang M-staging system15. Unfortunately, cytology exhibits limited sensitivity and sometimes cannot provide accurate information.Moreover, MB tumours evolve with time highlighting the need for longitudinal samples to characterise tumours at different time points during the progression of the disease. Despite current efforts, around 30% of patients relapse, locally or through metastatic dissemination to the leptomeninges, which is the major cause of mortality in children16. Tumour cells release DNA fragments that circulate in body fluids, and the analysis of ctDNA can provide information about the molecular profile and clinical evolution of cancer17. Our group and others have previously reported the detection of CSF ctDNA in adult brain malignancies23. The analysis of ctDNA in the CSF better reflects the brain tumour genomic alterations than in plasma, it can be used as a molecular diagnostic tool and the dynamic changes during patient monitoring recapitulate the disease course25.Altogether, this indicates that there is an underlying and ongoing need to improve the molecular characterisation and monitoring of MB. A relatively noninvasive method to characterise primary and relapsed tumours and monitor the evolution of the disease is required to assist the clinical management of MB patients. In this regard, liquid biopsies and in particular cell-free circulating tumour DNA (ctDNA) have opened an avenue of research27. In addition, CSF samples are routinely collected for cytologic analysis. However, despite the access to CSF, the study of CSF ctDNA has not yet been explored in the paediatric setting except for a few studies focusing on paediatric diffuse midline gliomas through the analysis of H3 mutations in the CSF ctDNA by droplet-digital PCR (ddPCR) or targeted Sanger sequencing29. In the current study, we detect ctDNA in the CSF but barely in the plasma of patients with MB. The analysis of CSF ctDNA reflects the genomic alterations of the tumour and allows the subgroup classification and risk stratification of MB patients. In addition, the study of follow-up CSF samples identifies minimal residual disease and reveals the genomic evolution at relapse. Altogether, the analysis of CSF ctDNA is a relatively noninvasive and reliable tool that could aid in the diagnosis, prognosis and monitoring of children with MB.Hydrocephalus is very common in paediatric patients with posterior fossa tumours like MB. In these cases and prior to surgical treatment of the tumour, CSF drainage is required to alleviate the intracranial pressure (ICP)In order to assess whether the analysis of CSF ctDNA could be a tool for the diagnosis and monitoring of MB patients, tumour, blood and CSF samples were collected from 13 patients Fig. .Fig. 1PrHydrocephalus was detected through MRI in all patients at the time of hospital admission and CSF drainage, through the implantation of a transitory external ventricular drain (EVD), was required to reduce ICP. Aiming to avoid the implantation of a definitive CSF shunt, 12 of the 13 patients underwent endoscopic third ventriculocisternostomy before the implantation of the EVD. During this intervention, the CSF sample was obtained and used for standard of care clinical measurements (cytology) and research purposes. In the following days, the patients underwent surgical resection of the posterior fossa tumour and additional CSF samples were obtained. Before the administration of chemotherapy, three patients required the implantation of an Ommaya reservoir  allowing for periodical CSF samples to be collected. For the remaining 10 patients, follow-up samples were acquired through a lumbar puncture.30.Whole-exome sequencing (WES) of matching tumour/normal DNA was performed and common MB germline and somatic driver mutations, focal copy number variants (CNVs), arm-level and whole chromosome gain/loss were identified Fig. . The fre5, and were assigned a predicted risk stratification (5-year PFS) based on the molecular subgroup and the presence of risk modifiers  histology)4 4 Fig. .The clinicopathological characteristics, molecular features, MB-subgroup classification and risk stratification of the 13 MB paediatric patients at the time of enrolment (12 at the time of first-diagnostic and 1 at the diagnostic of relapse (MB8)) are shown in Fig. We sought to evaluate whether ctDNA could be detected in the CSF of the 13 MB patients and how it compared to plasma ctDNA and standard of care CSF cytology. To facilitate ctDNA detection, somatic mutations were selected from the WES of matched tumour/normal samples based on variant allele frequency (VAF) and impact. Mutations were validated by ddPCR in the tumour/normal DNA and then the pre-surgery CSF and plasma cell-free DNA (cfDNA) samples were analysed.No cells were detected in the CSF from the cytologic analysis for any of the cases. However, ctDNA was detected in the CSF samples obtained before the surgical intervention for 76.9% (10/13) of patients. In contrast, ctDNA could not be detected in plasma, except for one patient albeit with a very low VAF (2.2%) Fig. . Altoget3) but no correlations were observed  when compared to the rest of the patients and were identified as outliers Fig. . To inveAt the time of completing this study, disease progression was not observed for 10/13 patients and CSF ctDNA was not detected during patient monitoring   of the mutations detected in the primary tumour sample with VAF > 5% could also be detected in the matching CSF ctDNA. Importantly, there was a significant correlation between the tumour VAFs and the ones obtained from the CSF ctDNA (R87) Fig. . This obThe analysis of CNVs, arm-level or whole chromosome gain/loss revealed a similar situation. The overall CNV landscape in the CSF samples resembled the one found in the matching tumours . Despite the restricted amount of cfDNA from MB5-CSF, key MB CNVs detected in the tumour could also be detected in the CSF Fig. .PTCH1 and TP53 mutation; MYCN and GLI2 amplification; SUFU deletion and 17p loss were detected and facilitated subgroup classification. The four patients were identified as MB SHH subgroup, for which residual disease, metastatic dissemination, LCA histology or MYCN amplification are risk modifiers indicators of very high risk4. Importantly, these results showed that the CSF ctDNA analysis could be used as a diagnostic tool to classify and risk-stratify patients.To determine whether the molecular alterations identified from sequencing the CSF ctDNA could provide diagnostic and prognostic information, patients were classified into a MB-subgroup and risk-stratified Fig. . MB commWe next sought to address whether the WES of CSF ctDNA could assist in the monitoring of the genomic tumour evolution of MB patients and provide information concerning response to treatment or disease progression. Follow-up samples of CSF and plasma were collected in order to determine whether the levels of CSF ctDNA could fluctuate with time during the course of the disease.SRCAP (SRCAP:p.R2444*) was validated by ddPCR  Fig. . When thSRCAP:p.R444* was PCR Fig. . The facPCR Fig. .Fig. 4CS2 while, interestingly, CSF ctDNA was still detected with VAF 7.6% for BMP5:p.R321* 13 days after surgery  MB. Postoperative imaging identified residual disease <1.5 cmied Fig. .Fig. 5CSPTCH1:p.L1024Pfs, TP53:p.N239Tfs, CDKN2B:p.G124D and KMT2A:p.W1199S 34. It is also important to highlight the CNV landscape  . A written consent, indicating that the samples obtained as standard of care in the clinical practice could be used for research, was obtained from the parents/legal representant in agreement with the declaration of Helsinki. The study was approved by the Vall d’Hebron University Hospital IRB.g centrifugation for 10 min. Both plasma and CSF samples were centrifuged at 3000 × g for 5 min and the supernatant were collected. cfDNA from plasma and CSF samples were extracted using the QIAamp Circulating Nucleid Acids kit (Qiagen). Genomic and cfDNA were quantified using the Qubit fluorometer.Tumour DNA was extracted from a fresh-frozen or paraffin-embedded section of the tumour biopsy using the QIAamp DNA mini kit (Qiagen) and the QIAamp DNA FFPE tissue kit (Qiagen), respectively. Germline DNA was extracted from peripheral blood cells using the QIAamp DNA mini blood kit (Qiagen). Peripheral blood was collected in K2EDTA-containing tubes (Vacutainer) and plasma was acquired following a 1600 × Whole-exome DNA sequencing (WES) was performed using 2.5 μg of matching tumour and germline genomic DNA samples of the 12 patients. Library preparation and enrichment was performed using SureSelect Human All Exon V5 (Agilent Technologies) and sequenced using Illumina paired-read sequencing platform HiSeq2500 . For one patient (MB13), WES was performed using the SureSelect XT HS Human Focused Exome 17.7 Mb as explained below.Given the limited amount of cfDNA available from the 13 patients, CSF-derived cfDNA from four patients underwent DNA sequencing (6–200 ng). For one patient (MB8), SureSelect Human All Exon V5 (Agilent Technologies) was used for the CSF-derived cfDNA obtained at relapse (51.6 Mb). For the remaining 3 patients, longitudinal CSF samples were obtained before surgical intervention of the tumour biopsy and during progression or follow-up. Matching tumour and germline (200 ng), and follow-up CSF samples (6–200 ng) were analysed using the SureSelect XT HS Human Focused Exome 17.7 Mb  for library enrichment and sequenced using Illumina platform NextSeq with a read length of 150 bp.39 and annotated using the Variant Effect Predictor (VEP)40. Germline mutations were extracted using BCFTOOLS and filtered using common SNPs from dbSNP. Copy Number Variants (CNVs) were detected using the program exome2cnv41. Normal samples were compared to each other to identify any region that could be altered only by the region’s own variability. A summary with the events was done to obtain the percentage of loss/gain of each chromosome.To process the sequencing data, FASTQ files were aligned to the human reference genome (hg19/GRCh37 hs37d5) using BWA-MEM, and PCR duplicates were removed. Alignment information is indicated in Supplementary Data Filtering was performed based on the following criteria. For SNVs, we focused on mutations affecting the protein coding sequence; a depth of coverage ≥20 was required to compare the tumour with the ctDNA VAF; if VAF > 5% in any of the samples from the same case, it was considered as a mutation, and its presence in the other samples was studied. To determine whether a sample contained a mutation that was detected in another sample from the same case, a VAF greater or equal to 1% was established as threshold. For germline mutations, we analysed coding regions with a mapping quality ≥50. To choose only heterozygous variants, we focused on 0.35 ≤ VAF ≥ 0.75, and mutations with a VAF greater or equal to 0.001 in the control cohort of the gnomAD database were removed to ensure that polymorphism were not present in the final set of mutations. For CNVs, only regions which included ≥10 probes were considered. Regions with a log2ratio greater or equal to 0.5 were considered gains, and lower or equal to −0.5 were considered deletions. For a more stringent analysis we used −1 ≤ LOG2RATIO ≥ 1 as a threshold to report MB CNVs.5. Mutations: BCOR, CTDNEP1, CTNNB1, DDX3X, GFI1, GFI1 act, GFI1B act, KBTBD4, KDM6A, KMT2C, KMT2D, PRDM6 act, PTCH1, SMARCA4, SMO, SUFU, TERT, TERT promoter, TP53, TP53 , ZMYM3. CNVs: GLI2 amp, MYC amp, MYCN amp, OTX2 amp, PTEN del, SUFU del. Cytogenetics: 1q+, 2+, 4−, 5+, 6−, 7+, 8−, 8+, 9p+, 9q−, 10−, 10q−, 11−, 13−, 13+, 14+, 14q−, 15−, 16−, 16q−, 17p−,17p+, 18+, 21−. i17q was excluded because it was not possible to calculate from the data. The number of events from each category were counted and when alterations belonged to distinct subgroups, the group with the most events was selected  divided by the WES panel size (Supplementary Data n = 46) to determine the quartile distribution. Samples followed a normal distribution , with the following column stats: min: 18%; 25% percentile: 29.5%; median 44.5%; 75% percentile: 65.5%, max: 100%. For the MB classification subgroup, we included the arm-level or whole chromosome gain/loss within the 75% percentile [covered 65.5–100%, black) and above the median [covered 44.5-65.5%, grey).The proportion of arm-level chromosomal gain/loss was calculated for those with log2 value >0.5 and <−0.5; and % chromosome arm loss >15%. For total chromosome gain/loss, both arms needed to be affected. To define a threshold, the proportion of chromosome gain/loss obtained from the 13 tumour samples were plotted  according to manufacturer’s protocols and the literature45.We used droplet-Digital PCR (ddPCR), a robust method with high sensitivity and precision, to determine the presence of ctDNA in the CSF. From the NGS analysis of paired tumour/normal DNA, mutations were selected based on the impact of the mutation, the VAFs and whether they were relevant to MB pathogenesis or identified as driver mutations by the Cancer Genome InterpreterFurther information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Reporting Summary


Over the past few decades, numerous examples have demonstrated that intrinsic disorder in proteins lies at the heart of many vital processes, including transcriptional regulation, stress response, cellular signaling, and most recently protein liquid‐liquid phase separation. The so‐called intrinsically disordered proteins (IDPs) involved in these processes have presented a challenge to the classic protein “structure‐function paradigm,” as their functions do not necessarily involve well‐defined structures. Understanding the mechanisms of IDP function is likewise challenging because traditional structure determination methods often fail with such proteins or provide little information about the diverse array of structures that can be related to different functions of a single IDP. Single‐molecule fluorescence methods can overcome this ensemble‐average masking, allowing the resolution of subpopulations and dynamics and thus providing invaluable insights into IDPs and their function. In this protocol, we describe a ratiometric single‐molecule Förster resonance energy transfer (smFRET) routine that permits the investigation of IDP conformational subpopulations and dynamics. We note that this is a basic protocol, and we provide brief information and references for more complex analysis schemes available for in‐depth characterization. This protocol covers optical setup preparation and protein handling and provides insights into experimental design and outcomes, together with background information about theory and a brief discussion of troubleshooting. © 2020 by John Wiley & Sons, Inc.Basic Protocol: Ratiometric smFRET detection and analysis of IDPsSupport Protocol 1: Fluorophore labeling of a protein through maleimide chemistrySupport Protocol 2: Sample chamber preparationSupport Protocol 3: Determination of direct excitation of acceptor by donor excitation and leakage of donor emission to acceptor emission channel The purpose of this article is to outline a general protocol for implementing single‐molecule fluorescence  techniques to study the conformational populations, binding interactions, and dynamics of proteins that do not possess a well‐defined tertiary structure—so‐called intrinsically disordered proteins (IDPs). The lack of structure allows IDPs to sample a plethora of structural states rapidly at any given time. The resulting interactions of IDPs are conceptually different from those of folded proteins: IDPs are generally involved in promiscuous interactions with moderate affinity, although there are a few exceptions . As the implementation of this method has the prerequisite that proteins must be labeled with donor and acceptor fluorescent moieties, we explain in Strategic Planning the most common labeling strategy together with details that must be attended to. In the same section, we also briefly discuss the key components of the optical detection instrument required before starting an experiment, although a detailed description of how to assemble this instrumentation is outside the scope of this protocol. For the interested reader, we provide Key References that can be consulted for more complete details about optical instrumentation, analysis, and labeling. We then continue with a detailed protocol for smFRET. The basic protocol includes subprotocols for data acquisition that will eventually yield more detailed characteristics of the system. We also provide support protocols for preparing a sample chamber and for measuring critical parameters affected by the optical setup and the sample of choice.In single‐molecule fluorescence measurements, one or more extrinsic fluorophores are generally attached to IDPs in a site‐specific, covalent manner. In this section, we will briefly discuss different site‐specific labeling schemes, labeling site design, controls, and choice of fluorophores. Furthermore, a method for labeling a protein is provided in Support Protocol The most common way to attach fluorophores to proteins is by maleimide chemistry. In this approach, a free thiol group  on the protein is reacted with a fluorophore that is derivatized with a maleimide group, forming a thioether bond. It is a selective reaction around pH 7.0, though above pH 8.0, primary amines are also labeled  is the only natural protein amino acid with a free thiol group, which makes it suitable for this site‐specific reaction. Moreover, Cys is a rare amino acid, and its underrepresentation can be used to advantage for site‐specific labeling  incorporation in conjunction with click chemistry labeling has also become a popular approach for labeling proteins of interest for single‐molecule fluorescence studies. This approach takes advantage of orthogonal tRNA and aminoacyl tRNA synthetase pairs that can recognize a specific uAA and incorporate it into the protein sequence in response to the presence of a unique codon. In bacteria, the amber stop codon is commonly used for this purpose due to its low abundance. Depending on the functionality of the uAA, the fluorophore, with a suitable click‐chemistry handle, is reacted with the protein —that is, the distance at which the FRET efficiency is 50%. Monitoring the distance between a donor and an acceptor therefore depends heavily on the specific donor‐acceptor pair's R0. When designing an smFRET experiment, a suitable donor‐acceptor pair choice should be made by taking into consideration two factors: (i) the donor absorption spectrum should overlap with the excitation laser wavelength, and (ii) the sequence separation, and the changes thereof, must be within the dynamic range of FRET efficiency values that can be probed with the chosen donor‐acceptor pair. It is admittedly nontrivial to design fluorophore attachment sites without a priori knowledge of approximate distances for IDPs; however, as a starting guess, one can predict the theoretical distance for an extended chain, which will be an overestimate for a native IDP in most cases.Inferring the distance between two fluorophores requires using two different fluorophores with overlapping donor emission and acceptor absorption spectra. This overlap is one of the deterministic factors of the Förster distance . Many commercially available small‐molecule dyes with different reactive handles fulfill the primary requirements for suitability for single‐molecule measurements: high quantum yield and photostability. These fluorophores span a wide range of excitation wavelengths, which is a crucial factor to consider in designing two‐ and multicolor experiments. Fluorophores vary in their relevant physical properties, such as net charge and hydrophobicity; therefore, they should be chosen to be the least disruptive possible for downstream applications. For example, if the proteins in question interact via charge‐charge interactions, then the influence of the fluorescent tag's charge must be characterized. Controls that investigate the effect of fluorophore incorporation should be implemented depending on the dominant force of protein‐protein interactions and physical property of the fluorophore.A confocal detection scheme for freely diffusing single molecules generally must satisfy the following criteria: (i) a light source for the excitation of fluorescent molecules in solution ; (ii) a sub‐femtoliter focal volume that, together with sub‐nanomolar sample concentrations, permits the detection of only single molecules; (iii) highly sensitive, single‐photon counting detectors that allow the number of emitted photons to be determined. Below and in Figure For the freely diffusing smFRET experiments used in this protocol, a laser beam is directed to an inverted microscope using mirrors and lenses for beam shaping. Neutral‐density (ND) filters (variable or step‐variable) can be included to vary excitation intensity on the optical table rather than at the laser source. Once in the microscope, the laser beam is focused into the sample using an objective with a high numerical aperture (NA) for excitation of the fluorophores in solution. Emitted fluorescence from one or several different fluorophores is collected through the same objective. Most of the stray excitation light that would otherwise saturate the detectors and overwhelm molecular signals is reflected by an (excitation) dichroic mirror; thus, mainly fluorescence emission light is transmitted. The light is then focused by a lens and passes through a pinhole placed at the focal plane, thereby reducing out‐of‐focus (background) signals and resulting in a sub‐femtoliter detection volume. Outcoming light from the microscope is directed to another (emission) dichroic mirror to spatially separate donor and acceptor emission photons. These photons are filtered once more with spectral filters to minimize the background signal that might have leaked from excitation light. Finally, lenses are used to focus each photon stream onto avalanche photodiode (APD) photon‐counting modules that provide excellent detection sensitivity. Detected photons are converted to electronic signals, which are directed to a counting device , and associated software  can be used to conveniently record photon counts/information. Photon counting data are then processed further depending as appropriate for the application. We discuss simple data processing routines in the Data Analysis sections of respective protocols.EFRET) distribution, which is a measure of the distance between a donor and an acceptor molecule attached to an IDP of choice. Although the absolute distances are complex to obtain, it is possible to evaluate the changes in FRET efficiency in a ratiometric manner, for instance, upon folding of an IDP, binding of ligands, introduction of denaturants or crowders, and so on. Simultaneous collection of donor and acceptor bursts is essential for performing smFRET experiments. The data analysis includes the filtering of bursts originating from the same molecules and the calculation of FRET efficiency. In its simple form, FRET efficiency values can be approximated by a Gaussian distribution that will vary between 0 and 1, depending on the proximity of the donor and acceptor. After fitting the curve with a Gaussian function, an average FRET efficiency value for the population can be obtained, which can be further treated by solving an integral for an appropriate probability density function for distance distributions. Here, the protein α‐synuclein and its labeling with specific dyes are used as an example, for clarity. However, general features of the protocol and variations thereof are provided at various points, so that the reader can adapt the procedure for their particular system.Measurement of the distance between two fluorophores (donor and acceptor) and changes thereof on an IDP can be achieved by smFRET. The strong distance dependence of smFRET can be used to distinguish distances in roughly the 30‐ to 70‐Å range for typical single‐molecule dyes. The output of a simple smFRET detection scheme is the FRET efficiency Objective lens cleaning tissue ∼10 nM stock solution of fluorescently labeled α‐synuclein Single‐molecule detection setup Tween 20–coated borosilicate chambered glass coverslips Software for data analysis IMPORTANT NOTE: While APD detection is ongoing, the ambient light exposure should be minimized to avoid damage to the APDs. Also, carefully follow any instructions for the specific instrument and laser that you are using.1Switch the laser power on and allow it to stabilize for at least 15 min. Switch the laser power meter on, and then monitor the laser power and fluctuations until the power has stabilized.2Adjust the laser power to a desired value (100‐150 µW should be sufficient) and remove the laser power meter from the beam path. If the light is not visible at the detection part of the setup, the intensity should be gradually increased.3Place a drop of water or oil (as appropriate depending on the objective type) on the objective with a dropper.4For alignment, place and align an appropriate surface (this can be the surface of a glass coverslip) at the sample plane to reflect excitation light back through the objective.5Before carrying out steps 6‐10, make sure the APDs are turned off.6Adjust the pinhole to be as large as possible, or remove the pinhole from the detection path. This is useful for initial rough alignment of APDs.7Remove spectral filters and dichroic mirrors (but not lenses or other beam‐shaping optics) from the beam path in the detection box.8With the help of a mirror, check whether the beam falls within the detector surface of the APD that detects light transmitted through the first dichroic mirror. This APD will be the least sensitive to any external alterations, as it does not require any reflection of light; hence, no mirror adjustments are needed. Change the APD position as needed until the beam is centered within the APD face.9Replace the first dichroic mirror and check the beam that is reflected. With a white card or piece of paper, check that the beam falls in the middle of any reflective mirrors, then toward the APD face. Adjust the dichroic mirror as needed to reflect the beam to a point that is roughly in the middle of the APD detector opening.10Adjust the second APD as in step 8. This operation can be extended to many APDs as needed; however, it is advisable to start from the least reflection‐dependent APD and proceed accordingly. We typically use two APDs for simultaneous detection of donor and acceptor photons, though additional detectors can be used to monitor more colors or photon properties such as polarization.11Place all the filters back in the beam path after the objective. This must be done before any of the following steps in which APDs are turned on.12Insert a pinhole of the desired diameter in the light path. Replace the reflective surface with a sample chamber filled with free donor fluorophore . High fluorophore concentrations and powers could lead to APD saturation or damage. Therefore, start with as low a donor concentration as possible (5 nM is sufficient). Begin with lower laser power (20 μW), and then increase to the desired value (step 2) as needed, and decrease the frequency  of data collection as necessary. A signal of ∼10‐50 kHz is sufficient for alignment.13Turn on the APDs and data acquisition program.14Adjust the pinhole position until the signal from the APDs is maximized. For the two‐APD detection system, at this point only one of them may respond, or both may respond but non‐coincidentally .In fact, synchronicity of APDs as a response to adjustment is an excellent measure to assess to see whether APDs are aligned correctly or not. If needed, reduce laser power, go back to the large/no pinhole setup (step 6), and adjust the APD positions to maximize signal, then go back to step 12.15Once the signal is maximized with the adjustment of the pinhole, individually adjust APDs further to maximize the signal.16Repeat steps 14‐15 iteratively, until no further improvements are recorded, and then turn off the ADPs and the data acquisition program.IMPORTANT NOTE: In a frequently used and stable single‐molecule setup, the alignment should not deviate significantly. We routinely perform steps 14‐16 for each new sample . Performing steps 4‐10 should be considered if the optical path is remodeled or a different donor‐acceptor pair is being used.NOTE: The data acquisition is described for measurements that utilize binned photons. Be sure to carefully follow any safety precautions needed for your specific system and experiment.17Prepare the α‐synuclein (or other protein) sample by diluting the sample with sterile‐filtered protein dilution buffer and adding suitable photoprotectors . The final concentration of dual‐labeled protein should be ∼100 pM. Add 200 µl of the solution to the coverslip chamber, and let it sit for 5 min before the measurement.18Place the coverslip in the instrument and adjust the objective to focus the beam in the sample (about 30‐50 µm from the surface).19Turn on the APDs and data acquisition program.20Adjust the acquisition frequency in the data acquisition program. The integration time is the reciprocal of this frequency; therefore, it should be tested according to the molecular motions expected within the sample. However, it is also important to remember that a higher frequency will yield a lower signal‐to‐noise ratio.21If a real‐time data visualization program is used, insert direct excitation, leakage, and signal threshold values. (See Support Protocol 22Run the experiment.23Once the experiment (or set of experiments) is complete, turn off APDs and laser, remove the sample chamber, and wipe off the objective with objective cleaning wipes.24For ratiometric smFRET measurements, subtract the contributions of background from the raw donor and acceptor photon streams.At low single‐molecule concentrations, an estimate of background intensity can be obtained by averaging all photon counts within one channel over the course of the data acquisition time. Compare these to average counts for samples lacking fluorescent molecules as needed.25Process background‐corrected photon streams to obtain intensities corrected for leakage and direct excitation. Once these are determined, use the procedure in Support Protocol 26T”) to the sum of donor and acceptor intensities within the same time bin (SUM = ID + IA > T) to identify qualifying bursts of donor and acceptor that originate from the same single molecule.Apply a threshold (“D >T AND IA > T) and “OR” (ID OR IA > T) criteria can be used, but those can be biased for specific EFRET values, and hence “SUM” is preferred  using Equation 1:Calculate the FRET efficiency  and Alexa Fluor 594 (acceptor) Organic solvent: dimethyl formamide (DMF) or dimethyl sulfoxide (DMSO)1 M Tris•Cl, pH 7.55 N NaClReducing agent: dithiothreitol (DTT) or tris(2‐carboxyethyl)phosphine (TCEP)or dry ice and ethanolLiquid nitrogen Buffer‐exchange device: NAP column  or HiTrap desalting column Centrifugal filter device  or dialysis device CAUTION: Fluorophores and fluorophore‐labeled proteins should be kept in the dark, avoiding direct exposure to light as far as practical to minimize photobleaching.1Dissolve 1 mg of each fluorophore in 200 µl of the organic solvent suggested by the manufacturer  and measure the stock concentrations by UV‐visible (UV‐Vis) spectrometry. Prepare 10‐µl aliquots and store up to 6 months at −80°C.Alternatively, the organic solvent can be evaporated using a vacuum centrifuge concentrator.2Prepare labeling buffer consisting of 20 mM Tris•Cl and 150 mM NaCl, pH 7.5. Sterilize it by passing it through a 0.2‐μm filter.See the manufacturer's instructions for buffer‐reagent compatibility. High concentrations of chemical denaturants are generally suitable; however, for any buffer composition, the pH should lie between 6.5 and 7.5 for the maleimide coupling reaction. Additionally, thiols such as DTT should be excluded , as they will react with the maleimide dye. For labeling with fluorophores that bear a net charge, it is advisable to include salt (100‐300 mM NaCl) in the labeling buffer to screen nonspecific charge‐charge interactions.3Dissolve the α‐synuclein (or other protein of interest) in the labeling buffer if lyophilized. Otherwise, buffer exchange it into the labeling buffer using gel filtration  or a centrifugal filter device.The labeling efficiency is higher at relatively high starting concentrations of protein, even if single‐molecule fluorescence measurements require few molecules. Therefore, before performing the buffer exchange, concentrate the protein solution to at least 50 μM with a centrifugal filter device and verify the concentration by measuring the UV absorbance at 280 nm.  Calculate the percentage labeling efficiency using Equation 4aAdd reducing agent and incubate the protein at room temperature for 1 hr to reduce any intra‐ and intermolecule disulfides. We suggest adding at least 10 mM dithiothreitol (DTT) or 0.5 mM tris(2‐carboxyethyl)phosphine (TCEP).If TCEP is preferred, make sure that the stock solution is pH adjusted.5aIf DTT is used, remove it from the solution by buffer exchange using devices outlined above. Any remaining DTT will react with maleimide fluorophores. If TCEP is used, it can remain in solution.6aAdd 3‐10 mole equivalents of the fluorophore dropwise to the protein solution and ensure homogeneous mixing by gentle pipetting. Incubate 1 hr at 37°C, 2 hr at room temperature, or overnight at 4°C.7aRemove the excess fluorophore by buffer exchange. If centrifugal filters are used, make sure the retentate is washed sufficiently so that no trace dye is detectable by UV absorbance of the flowthrough. Dialysis can be used for larger volumes.Perform these steps instead of the single‐fluorophore labeling detailed above to obtain dual‐labeled α‐synuclein (or other protein) samples. Steps 4a and 5a of the single‐fluorophore labeling procedure are essentially the same for double Cys‐fluorophore labeling.4bAdd 1 mole equivalent (or substoichiometric amounts) of donor fluorophore dropwise to the protein solution. Ensure homogeneous mixing by gentle pipetting. Incubate for 1 hr at 37°C, 2 hr at room temperature, or overnight at 4°C.Alternatively, at this point 5 mole equivalents of the acceptor can also be added. If this “one‐shot” approach is taken, purify the donor‐acceptor pair from other permutations present in the sample by HPLC or high‐resolution ion‐exchange chromatography.5bIf only donor was added above, separate from free dye as in step 7a. It is advisable to reduce the protein again if step 4b is performed overnight, and the reducing agent (DTT) should be removed before the acceptor fluorophore is added.Alternatively, single‐labeled protein can be separated from unlabeled protein at this step by HPLC or a high‐resolution ion exchange chromatography.6bAfter donor‐only single‐labeled protein is obtained, measure the concentration of the protein. Add a 5× molar excess of acceptor fluorophore and incubate 1 hr at 37°C, 2 hr at room temperature, or overnight at 4°C.7bPerform step 7a again.8Measure the labeling percentage by UV‐Vis spectroscopy according to the formulaλ,F is the absorbance value at the fluorophore's maximum absorbance wavelength; εF and εP are the fluorophore and protein extinction coefficients (in M−1 cm−1), respectively; CF is the ratio of fluorophore extinction coefficients at 280 nm and the maximum absorbance wavelength ; and l is the path length in centimeters. Labeling yields may be >90%, but vary depending on protein and label site.where Abs9If needed, further purify the labeled proteins by HPLC or high‐resolution ion‐exchange chromatography.This is especially useful if the labeling efficiency is low, which would be indicated by the measurement of a low yield in step 8 above for the donor‐labeled protein, or by a large relative population of the “zero‐peak”  in the smFRET histogram for the donor‐acceptor‐labeled protein.10Verify the correct mass by LC‐MS (or other MS method).11Aliquot the labeled protein and snap‐freeze it in liquid nitrogen or a dry ice/ethanol bath.A concentration of 50 µM is usually appropriate.Support Protocol 2Sample chambers for single‐molecule fluorescence measurements exist in various forms. Chambered coverslip systems that are tailored for growing and imaging cells serve well for single‐molecule measurements, as they come in multiwell formats. The ability to conduct measurements of different samples without changing the sample chamber on the microscope, the accessibility of well contents during the experiment, and the short preparation time make multiwell systems advantageous over sandwich‐type glass‐coverslip system.Avoiding nonspecific protein adsorption is crucial to performing single‐molecule fluorescence experiments as limited number of molecules are available to freely diffuse in solution. Nonspecific adsorption is reduced by treating the chamber surfaces with detergent. Many sample chambers can be prepared at once and stored in dry conditions at room temperature for 1 month in a covered box wrapped in tissue paper to prevent dust from sticking to surfaces.10% (v/v) Tween 20 see Milli‐Q‐filtered waterBorosilicate chambered glass coverslip  or other appropriate sample chamber(s)Vacuum suction system1Fill each surface or well with 10% Tween 20 and incubate it 30 min at room temperature.2After incubation, discard the Tween 20 solution from the chamber, ideally using a vacuum suction system.Avoid Tween 20 contamination on the objective facing side of the glass surface.3Wash the chamber thoroughly with Milli‐Q‐filtered water.4Dry the chambers under light vacuum and store up to 1 month at room temperature.A strategy frequently used to prepare the chamber is to sandwich a spacer of double‐sided tape or Parafilm between two coverslips or a glass slide and a coverslip. A detailed protocol for chamber preparation is documented by Joo and Ha for single‐molecule fluorescence in TIRF mode , and (iii) direct excitation of acceptor by excitation (Dex). These terms are dependent on both the inherent photophysical properties of the donor and acceptor pair and characteristics of the instrument (including filters and dichroic mirrors). To determine the leakage contribution, use a proper donor‐only labeled sample.The measured signals depend on contributions from emission of donor and acceptor dyes in the donor and acceptor detection channels respectively, arising from (i) donor excitation, (ii) donor emission (arising from donor excitation) leakage into the acceptor channel (Donor‐only and acceptor‐only labeled protein samples (Support Protocol Tween 20–coated borosilicate chambered glass coverslip (Support Protocol Single‐molecule detection setup (see Strategic Planning)1Place an appropriate concentration of donor‐only labeled sample into the sample chamber. The concentration of the donor‐only sample must be sufficiently high for a reliably measurable donor emission to leak into acceptor channel, but it should not be in the saturation or damage range for the donor channel.See the suggestions of the APD manufacturer concerning the maximum count rate.2RD) and acceptor (RA) can be defined as follows . Perform only background correction by subtracting the average number of photons for both donor and acceptor channel, recorded for buffer‐only sample. Raw photon numbers of donor /(1 – L), which can be solved for Dex with a known L.Assuming that there is no FRET between free dyes , the 20 mM Tris•Cl150 mM NaCl2 mM DTT, pH 7.5Add 2‐mercaptoethanol to a final concentration of 140 mM as photoprotectant. Filter sterilize with a 0.2‐µm sterile filter .Prepare fresh before each experiment.Mix Tween 20  and Milli‐Q‐purified water in 10:90 volume ratio—be careful to ensure proper mixing, as Tween 20 is denser than water. Sterile filter and store at room temperature for as long as the solution remains clear and homogeneous.A considerable proportion of the eukaryotic genome encodes proteins that contain regions without well‐defined, three‐dimensionally folded structures ; small‐angle x‐ray scattering (SAXS) in combination with computational tools  where proteins are freely diffusing in solution and fluorescence is collected via confocal detection, and (ii) where proteins are tethered to a surface and fluorescence is detected in a total internal reflection geometry.kT(r) from D to A is given bykD is the excited state lifetime of the donor in the absence of acceptor, R0 is the Förster radius, and r is the distance from D to A. The energy transfer rate is strongly dependent on the distance between D and A, being proportional to r–6. R0 is dependent on the D‐A pair  and is expressed asOne of the most frequently leveraged single‐molecule fluorescence applications is Förster resonance energy transfer (FRET). FRET allows accurate measurements of the distance between two fluorescent molecules, namely a donor (D) and an acceptor (A), that are within a few nanometers’ proximity. Energy from the excited state of donor molecules is transferred via a dipole‐dipole coupling mechanism to acceptor molecules that absorb at wavelengths overlapping those of the donor emission. The rate of energy transfer QD is the quantum yield of the donor in the absence of an acceptor, N is Avogadro's number, n is the refractive index of the medium, and J is a quantitative measure of spectral overlap between donor emission and acceptor absorption, known as the overlap integral. In calculating J, care should be taken to normalize the spectrum to unity with respect to its area  and acceptor (IA) fluorophores under continuous‐wavelength laser excitation of the donor . Here c is the closest distance between the dyes, lc is the contour length of the protein, and P(r) is a normalized probability density function, modeled based on various polymer models available. An excellent review of these models and their application to IDP systems in conjunction with smFRET measurements can be found elsewhere  using the sensitized emission of A upon excitation of D  and (ii) using the changes in the lifetime of D in the presence of A. The principal hardware differences between the two modes are the time resolution and photon‐counting capabilities of the setup, and the need for a sub‐nanosecond pulsed laser for mode 2.As discussed above, Mode 1, known as “ratiometric detection,” is easier to implement than mode 2. In mode 1, a counter‐timer board (or other counting device) is used to count photons within time bins (integration time). Summed photons in a bin are tentative constituents of a burst, depending on the number of photon counts. Evidently, the bursts will contain more photons when the integration time is longer. However, a longer integration time also results in the inclusion of additional background and signals from multiple molecules, giving rise to decreased signal‐to‐noise ratio and contaminated single‐molecule signals. Hence, the integration time must be chosen carefully. Historically, smFRET data have been collected with integration times ranging from 0.2 to 1 ms  device. This device registers both the arrival times of individual photons compared to the start of the experiment and the time lag between each photon and the previous exciting laser pulse. With these two parameters, the overall time trajectory of photon arrivals can be reconstructed for D and A channels separately, and correlation functions can also be computed. Furthermore, after selection of eligible florescent bursts, fluorescence decay histograms can also be computed and decay rates calculated  that involves the use of alternating laser excitation (ALEX). ALEX is a method used in combination with single‐molecule FRET that introduces a second excitation source to report on the presence of the acceptor fluorophore. ALEX was developed by the Weiss laboratory to address the difficulty of distinguishing low‐FRET species, with an acceptor fluorophore present but distant from the donor, from single‐labeled donor‐only species ; direct excitation of acceptor (fAexAem); emission from donor resulting from excitation of acceptor, which should be equal to background (fAexDem); and emission from acceptor resulting from excitation of donor, or in other words, sensitized emission by FRET (fDexAem). EFRET in ALEX experiments is calculated identically to the way it is in single‐excitation FRET experiments. Rewriting Equation During an ALEX experiment, molecules diffusing through the focal volume are alternately excited by two different lasers, each capable of exciting either the donor or the acceptor dye. This yields four streams of photon emission data described by the excitation (ex) wavelength used and the emitting (em) fluorophore measured: direct excitation of donor (S), where values approaching 1 represent donor‐only molecules, values approaching 0 represent acceptor‐only molecules, and intermediate values represent molecules dually labeled with a FRET pair; thus,The additional information provided by directly exciting the acceptor molecule is incorporated into the stoichiometry ratio . However, in some cases, despite having a flexible linker, the dye can interact with amino acid side‐chain moieties, constraining the rotational freedom and skewing κ2 from 2/3. To test whether κ2 = 2/3 is a reasonable assumption, steady‐state ensemble or single‐molecule anisotropy values should be measured. Ideally, anisotropy values should be <0.15 in all conditions tested in a single‐molecule experiment.The orientation factor, instrument depends on the detection efficiencies (γinstrument = ηA/ηD) and γsystem depends on the quantum yields of donor and acceptor (γsystem = ΦA/ΦD), where ηD,ΦD and ηA,ΦA are the detection efficiencies and quantum yields of donor and acceptor, respectively.This parameter is a correction factor based on the detection efficiencies of detectors and quantum yields of the dyes. Although this parameter may not fluctuate very much, it is important to measure it for more precise distance measurements. γ essentially has two contributors, as follows , and centered at the average EFRET  will be resolved as two separate Given the complexities in interpreting peak widths, inferences about conformational dynamics should preferably be validated with another method that directly quantifies fluctuations, such as FCS or FRET‐FCS.Impurities in buffer components may greatly increase the noise. In IDP research, it is quite common to vary the salt or denaturant concentrations to elicit the behavior in line with polymer physics; therefore, those components must be purchased carefully and tested. A good practice is to note the manufacturers from the publications from other laboratories conducting single‐molecule FRET studies.Occasionally, during the instrument alignment process, a local minimum in the maximum number of counts will be encountered, which will eventually skew all FRET efficiencies. Ideally, a sample with known FRET efficiency must be measured  to assess the viability of the alignment process.The burst shape is an important indicator of sample condition. In an smFRET experiment, the signal will be at the noise level most of the time with occasional single, sharp bursts that ideally will appear in both donor and acceptor channels at the same time. Unusually intense or lengthy bursts can appear when the sample contains impurities that diffuse more slowly than a protein. These can be nonspecific aggregates of the IDP in question, which emit continuously while traversing the focal volume, whereas a single molecule's diffusion time is much faster. In this case further purification or optimization of the experimental conditions may be needed to avoid artifacts.EFRET values, centered around one or multiple peaks. By sequentially titrating the binding partner, salt, denaturant, or crowder, one can observe and quantify exchange between peaks (subpopulations) or shifts in peak position and/or peak shape. See Figure EFRET value, as well as the peak area and width, which can then be plotted against the effector concentration for further analysis and interpretation. Several additional types of analyses are possible, some of which are discussed in papers mentioned in the Critical Parameters subsection on Peak broadening and shape and listed in the Key References.The Basic Protocol will generate a histogram of Once the required protein constructs are expressed and purified, the Basic Protocol and labeling can be completed within a few weeks, including some degree of troubleshooting and an initial set of data collection and analysis. Initial beam alignment  usually takes <1 hr. Subsequent minor alignment optimization typically takes only a few minutes. Once the data acquisition process is streamlined, data acquisition takes <1 hr per sample. Multiwell chambers save a few minutes per sample preparation; however, a strength of the open multiwell chambers is the ability to carry out titrations or switch back and forth between multiple samples in the chamber. The experimental timeline can extend substantially if the protein reagents require more elaborate labeling or purification schemes, and to optimize experimental conditions for more complex IDP systems. For simple and easy‐to‐handle IDP systems, following optimization, the Basic Protocol may be completed in 2‐3 days.