Assays such as single cell ATAC-seq (scATAC-seq) offer an opportunity to interrogate cellular level epigenetic heterogeneity through patterns of variability in open chromatin. However, these assays exhibit technical variability that complicates clear classification and cell type identification in heterogeneous populations. We present  Single cell ATAC-seq (scATAC-seq) data reveals cellular level epigenetic heterogeneity but its application in delineating distinct subpopulations is still challenging. Here, the authors develop scABC, a statistical method for unsupervised clustering of scATAC-seq data and identification of open chromatin regions specific to cell identity. However, datasets arising from such technologies are difficult to analyze due to the inherent sparsity. In particular, consider scATAC-seq, designed to interrogate open chromatin in single cells. Open sites in a diploid genome have at most 2 chances to be captured through the assay and only a few thousand distinct reads are generated per cells, resulting in a very low chance that a particular site is captured by the assay. Consequently, it is difficult to determine whether a region is absent in an individual cell due to the lack of openness or due to the sparse nature of data. This creates a challenging task in delineating distinct sub-populations, as only a few genomic regions will have overlapping reads in a large number of cells. To avoid this issue, many studies perform FACS sorting to identify subpopulations, followed by bulk sequencing to determine genomic regions of interest and guide the single-cell analysis. If the population is unknown or marker genes are unavailable, then sub-population specific analysis becomes impractical with these techniques.Recent advances in single cell technologies such as scATAC-seqscABC). In contrast to previous works4 that demand predefined accessible chromatin sites, our procedure relies solely on the patterns of read counts within genomic regions to cluster cells. It requires two inputs: the individual single cell mapped read files and the full set of called peaks . We apply our method to publicly available scATAC-seq data4, as well as a true biological mixture to show that our approach can cluster cells with similar epigenetic patterns and identify accessible regions specific to each cluster. We further demonstrate that the cluster specific accessible regions determined by scABC have functional meaning and are capable of determining cellular identity. In particular, we show that these cluster specific accessible regions are enriched for transcription factor motifs known to be specific to each subpopulation and that, through association with scRNA-seq data, they can lead to the identification of subpopulation specific gene expression.To combat these challenges and allow for the de novo classification of individual cells by their epigenetic signatures, we present a statistical method for the unsupervised clustering of scATAC-seq data, named single cell Accessibility Based Clustering (scABC first weights cells by (a nonlinear transformation of) the number of distinct reads within peak backgrounds and then applies a weighted K-medoids clustering5 to partition the cells into distinct groups (see Methods for details). scABC uses the ranked peaks in each cell to perform the clustering rather than the raw counts to prevent bias from highly over-represented regions. We found that this usually sufficient to cluster most cells, but a few problematic cells seem to be misclassified. To improve the classification, we calculate landmarks for each cluster. These landmarks depict prototypical cells from each cluster and are characterized by the highest represented peaks in each cluster, which we should trust more than the noisy low-represented peaks. scABC finally clusters the cells by assignment to the closest landmark based on the Spearman correlation  may merge when the representation of one falls below 5% of the total population . chromVAR calculates deviations, essentially z-scores for TF motif enrichment that are normalized for background accessibility and other biases such as GC content. We found that the most active TFs are typically specific to one or two clusters, identifying active TFs in every cell type . We defined a gene to have a cell type specific open promoter if any open peak with an scABC p-value of <10−6 overlapped more than 400 base pairs in the gene promoter, defined as the region 5 kb upstream of the primary FANTOM516 TSS.In contrast to narrow peaks, broad regions are more suited to demonstrate functional DNA elements such as promoters and enhancers17, clustering based on all genes did not clearly separate the two cell types in the first two principal components. When genes associated with cell-type specific open promoters were employed, the separation became extremely obvious. Similar patterns were observed when using t-SNE plots  from mouse embryonic stem cells (mESCs). We next differentiated EBs using retinoic acid (RA) treatment and performed scATAC-seq on day 4 of the development (Methods). We generated a single 96-well plate and obtained 95 cells that pass quality control (Supplementary Note).21. However, when three-dimensional EBs are treated with RA, previous studies23 have suggested that the outer layer of EBs expresses marker genes that are characteristic of visceral endoderm cells during early mouse development. We therefore hypothesized that the RA-treated EBs are a heterogeneous mixture, consisting of both visceral endoderm and neural ectoderm cells. To confirm this heterogeneity in terms of chromatin accessibility, we applied scABC to the 95 cells and obtained K = 2 clusters , which have been shown to function in the development of mouse motor neurons36. In contrast to cluster 1, TFs specific to cluster 2 are essential for visceral endoderm differentiation, such as GATA factors37, HNF1A/B39, and the AP-1 family41 . The TF enrichment analysis suggests that scABC clearly distinguishes neuroectoderm (67 cells) from visceral endoderm (28 cells), two sub-populations with the same origin (mEB) in early embryonic development.It is well known that RA-treated mESCs are induced to differentiate into neuronal cell types14 (the closest publicly available RNA-seq to day 4). We first used TOMTOM tool42 to identify TFs with similar motifs to those enriched in cluster 1 and 2  and then selected a subset of them that are highly expressed in bulk RNA-seq data (FPKM ≥ 10). Interestingly, the majority of motifs that were found enriched in cluster 1 and 2 are associated with expressed TFs. Specifically, the expressed genes BHLHE22, SHOX2, GBX2, HOXB2/3/5, LHX4, MNX1, NEUROG2, NKX6-1/2, OLIG1/2, and POU3F1 to POU3F4 are active (in terms of motif enrichment) in cluster 1 while FOS, FOXB1, GATA4, JDP2, JUN, and JUND are active in cluster 2. These findings indicate that both neuroectoderm and visceral endoderm sub-populations are active at RNA level.Since chromVAR only reflects motif enrichment and cannot distinguish TFs with similar DNA-binding motifs, we next sought to narrow down the list of TFs using bulk RNA-seq data at day 6 of the developmentscABC performance, we tested its ability for detecting developmental stages of cancer evolution. Corces et al.4 sequenced individual monocytes and lymphoid-primed multipotent progenitors (LMPP) from healthy donors and leukemia stem cells (LSC) and leukemic blast cells (blast) from donors with acute myeloid leukemia. Notably, this dataset is extremely sparse compared to the in silico mixture of 6 cell lines . Our method detected K = 2 clusters, which resulted in a clear separation of the cells into a monocyte dominated cluster and a LMPP dominated cluster with blasts predominantly clustered with monocytes and LSCs mainly clustered with LMPPs  using read counts in peaks and binned counts over long intervals , as well as K-means on the log transcripts per million matrix , a common scRNA-seq clustering method.scABC quality control (Supplementary Note). We found that simple K-mediods had a slightly higher misclassification rate  and a community structure clustering method based on the infomap algorithm47. Applying these methods to the in silico mixture, SC3 did not distinguish the BJ cells from HL60 cells, despite our results indicating that BJ cells are well separated from all other cell types  and scABC, we downsampled each cell line and found that scABC is able to identify smaller subpopulations .To clarify the differences between simple scABC’s method of determining cluster specific peaks, we used peaks differentially open in the respective bulk data as a gold standard and compared scABC to an existing method for identifying differentially expressed genes in single cell RNA-seq, SCDE48 (Supplementary Note). We found that the majority of cluster specific peaks identified by scABC are differentially open in the respective bulk data and the overlap was much larger than the differentially expressed peaks of SCDE . The weight for cell i is defined asc and λ are tuning parameters. As defaults, we use the median of the background and 0.1, respectively. We found that the performance of the clustering is robust to a wide range of {c, λ} , K the number of clusters, C the cluster assignment, and ik the medoid for cluster k, i.e. a cell used as the cluster center. The clustering assignment is given by the solution tod(·) in general represents the dissimilarly between a pair of samples. We use 1–Spearman’s rank correlation as the dissimilarity measure, and refer to the Spearman rank correlation as the similarity measure. The problem above is solved by the Partitioning Around Medoids (PAM) algorithm51 as implemented in the R package WeightedCluster5.Let P peaks with the highest read counts to obtain the landmark for each cluster identified in the previous step. As a default we set P = 2000.Landmarks: We sum the reads across the cells within a cluster and select the Re-clustering using landmarks: To refine the clustering results, we re-cluster the cells by assigning each cell to the landmark with the highest Spearman’s rank correlation using the union of all landmark peaks.K-medoids algorithm requires the number of clusters K in advance. We determine K through the gap statistic52 with a few modifications to better capture the data structure of single cell experiments, particularly sparsity and cell heterogeneity  βmle. Let α quantile of the empirical cdf; and let zα be the 1 − α standard normal quantile. The empirical prior estimate for the standard deviation is calculated asq = 0.05 in practice. Details for computing βmle are described in the Supplementary Note.Empirical prior estimate: The normal prior enables empirical Bayes shrinkage on k− = {1, …, K}k− represent the set {1, …, K} except for the kth element. To test whether peak r is specific to cluster k, we considerHypothesis testing: Suppose Γ54, the null hypothesis can be broken into K − 1 simpler null hypotheses Hk′0 : βrk ≤ βrk′, with k′ ∈ Γk−. For each null hypothesis, the Wald test statistics is β and SE(·) the MAP estimated standard error, which depends on both the observed data and prior estimates. The rejection region for Hk′0 with size α isH0 with level α isp-value for H0 as max{pk′, k′ ∈ kΓ−}, with pk′ indicating the p-value for Hk′0.Following the intersection-union testCell culture: Mouse ES cell lines R1 were obtained from ATCC. The mESCs were first expanded on an MEF feeder layer previously irradiated. Then, subculturing was carried out on 0.1% bovine gelatin-coated tissue culture plates. Cells were propagated in mESC medium consisting of Knockout DMEM supplemented with 15% Knockout Serum Replacement, 100 μM nonessential amino acids, 0.5 mM beta-mercaptoethanol, 2 mM GlutaMax, and 100 U/mL Penicillin-Streptomycin with the addition of 1,000 U/mL of LIF .55. Trypsinized cells were suspended in differentiation medium (mESC medium without LIF) to a concentration of 37,500 cells/ml. 20 μl drops (750 cells) were then placed on the lid of a bacterial plate and the lid was upside down. After 48 h incubation, EBs formed at the bottom of the drops were collected and grown in the well of a 6-well ultra-low attachment plate with fresh differentiation medium containing 0.5 μM RA for 4 days, with the medium being changed daily.Cell differentiation: mESCs were differentiated using the hanging drop method1 with the following modifications. The EBs were first treated with StemPro® Accutase Cell Dissociation Reagent (Thermo Fisher) at 37 °C for 10–15 min, followed by vigorous pipetting for another 10 min. The cells were passed through 20 μM cell strainer (pluriSelect) to remove un-dissociated EBs. Before loading, the cells were washed three times in C1 DNA Seq Cell Wash Buffer (Fluidigm). In total 9 μL cells at a concentration of 400 cells/μL were combined with C1 Cell Suspension Reagent at a ratio of 3:2 and 10 μL of this cell mix was loaded on to the 10–17 μM Fluidigm IFC. Single cells were captured using the “ATACseq: Cell Load and Stain (1861x/1862x/1863x)” scripts. After cell capture, IFC was transferred to a Leica CTR 6000 microscope for imaging, followed by Tn5 transposition and primary 8 cycles of PCR using the “ATACseq: Sample Prep (1861x/1862x/1863x)” scripts. The entire volume (3.5–5 μL) of the amplified transposed DNA was transferred to a 96-wll plate containing 10 μL of C1 DNA Dilution Reagent. In the 96-well plate, harvested libraries were further amplified in 50 μL PCR  using the following PCR conditions: 72 °C for 5 min; 98 °C for 30 s; and total 14 cycles of: 98 °C for 10 s, 72 °C for 30 s, and 72 °C for 1 min. The PCR products were pooled together (4.8 mL) and the pooled library was purified on a single MinElute PCR purification column (Qiagen) and eluted in 20 μL of Elution Buffer. Libraries were quantified using qPCR prior to sequencing using Illumina NextSeq 500 (paired-end 75 bps).scATAC-seq: We followed the scATAC-seq protocol published by Buenrostro et al.scABC package is available as an open source R package at https://github.com/timydaley/scABC.The GSE107651 Other datasets used in this work are cited in the paper, with the accession codes provided in Supplementary Note.The scATAC-seq data generated from RA-treated mESCs have been deposited in the Gene Expression Omnibus (GEO) under the accession number Supplementary Information


Pattern separation is a process that minimizes overlap between patterns of neuronal activity representing similar experiences. Theoretical work suggests that the dentate gyrus (DG) performs this role for memory processing but a direct demonstration is lacking. One limitation is the difficulty to measure DG inputs and outputs simultaneously. To rigorously assess pattern separation by DG circuitry, we used mouse brain slices to stimulate DG afferents and simultaneously record DG granule cells (GCs) and interneurons. Output spiketrains of GCs are more dissimilar than their input spiketrains, demonstrating for the first time temporal pattern separation at the level of single neurons in the DG. Pattern separation is larger in GCs than in fast-spiking interneurons and hilar mossy cells, and is amplified in CA3 pyramidal cells. Analysis of the neural noise and computational modelling suggest that this form of pattern separation is not explained by simple randomness and arises from specific presynaptic dynamics. Overall, by reframing the concept of pattern separation in dynamic terms and by connecting it to the physiology of different types of neurons, our study offers a new window of understanding in how hippocampal networks might support episodic memory. However, strong recurrent excitation severely limits the number of patterns that can be stored without overlap5. Such overlap would lead, when a partial cue common to several patterns is presented, to the reactivation of many patterns and thus to confusion or confabulation. To avoid these interferences, the Hebb-Marr framework proposes that redundancy between input patterns is reduced before they are stored. This process of transforming similar input patterns into less similar output patterns is termed pattern separation6.How does the brain allow us to discriminate between similar events in our past? This question is a central challenge in the neurobiology of memory and remains elusive. To prevent confusion between memories that share similar features, the brain needs to store distinct activity patterns to represent distinct memories. In the influential Hebb-Marr framework of episodic memory4. Indeed, DG is ideally located to do this, receiving signals via the major projection from entorhinal cortex (EC), the perforant path (PP), and sending signals to CA3 via the axons of granule cells (GCs)7. In addition, behavioral studies have shown that DG lesions impair mnemonic discrimination12 and several experimental reports have shown that similar environments or events are represented differently in the DG17. However, this separation of DG representations could be inherited from upstream structures (e.g. EC) and simply reported by DG. Therefore, a rigorous demonstration that pattern separation is performed by DG requires simultaneous knowledge of its inputs and outputs6. Some electrophysiological studies suggest that EC spatial representations are on average more correlated than in DG19, but the recorded EC neurons were unlikely to contact the recorded DG neurons, and were not recorded at the same time: a direct test of whether DG itself performs pattern separation on EC inputs is thus still lacking.Theoretical models suggest that the dentate gyrus (DG) performs pattern separation of cortical inputs before sending its differentiated outputs to CA323. Immediate-early genes expression studies have confirmed that distinct events drive plasticity in different populations of GCs24 and that overlap in these representations causes mnemonic interference25. In contrast, in vivo single-unit recordings in the DG found that similar contexts are represented by the same population of active neurons, but differences are encoded by different spatially tuned firing patterns17.Another difficulty in studying pattern separation is in defining the nature of “activity patterns”. Previous studies have focused on spatial patterns of “active neurons”, with little reference to the dynamics of neural activity. For example, computational models predict that DG separates overlapping populations of active EC neurons into less overlapping populations of active GCs6. For example, because in vivo recordings suggest that the same neurons can be used to code different environments17, it is possible that pattern separation is performed at the level of single GCs, each disambiguating the activity patterns that it receives. Such disambiguation could be done by changing firing rates, or alternatively, by changing spike timing. Previous experimental investigations of pattern separation in DG examined population vectors of place fields averaged over minutes26, but place cells also carry information at shorter timescales30. So far, pattern separation has not been well characterized on the scale of milliseconds, and never where patterns are explicitly afferent and efferent trains of action potentials.These conflicting results show that pattern separation could correspond to different computations depending on the type of patterns investigated, and that multiple forms of pattern separation could in theory be implemented by the DGThus, whether the DG network per se reduces the overlap between similar inputs, and how it performs this computation remains a mystery, especially at short timescales. Here, we set out to test the hypothesis that the DG performs pattern separation of cortical spiketrains at the level of single GCs. We designed a novel pattern separation assay in acute brain slices to take advantage of the experimental control afforded to slice electrophysiology. Complex input spiketrains of varying similarities were fed into the DG via its afferents, and the output of a single GC was simultaneously recorded, allowing the first direct measure of pattern separation (by comparing input similarity versus output similarity), on timescales relevant to neuronal encoding and synaptic plasticity. Finally, we explored whether other cell types in the DG and CA3 exhibited this form of pattern separation and investigated the role of neuronal noise and synaptic dynamics in supporting this computation.w of 10 ms (unless otherwise specified). We generated sets of Poisson input spiketrains , with each set having an average correlation Rinput   using a binning window τput Fig.  (102 recut) Fig. .Figure 1output was lower than the Rinput of the associated input set, indicating a decorrelation of the output spiketrains compared to their inputs   Fig.  or hilarts) Fig. .Figure 3input, Routput) were significantly different between FSs and GCs, with the Routput of simultaneously recorded GCs always lower than their corresponding FS  appeared only slightly different from GCs with this analysis  distribution was slightly but significantly lower than for GCs recorded in similar pharmacological conditions  (Methods – Pattern separation experiments) and recorded their output spiketrains in response to direct stimulation of the GCL, i.e. in response to its input from the DG Fig. . Due to ets Fig.  and addiets Fig. . PCs firngs Fig. . Despiteons Fig. . This inw from 5 ms to 250 ms and performed a finer grained analysis using pairwise Rinput and associated pairwise Routput instead of the average across the ten pairs of input trains , whereas FSs just show weak or no pattern separation.This analysis confirmed that, at short timescales, FSs exhibit less pattern separation than GCs Fig.  and reveput Fig. , which wput Fig. . At longput Fig. . InteresConcerning CA3 PCs, they still exhibit high levels of temporal pattern separation at long timescales and the difference with GCs increases Fig. . InteresOverall, these results show that among the tested DG celltypes, GCs exhibit the highest levels of temporal pattern separation of cortical spiketrains across all timescales. Our findings also suggest that the high level of separation by the DG is amplified in CA3.To determine what mechanisms might support temporal pattern separation in GCs, it is necessary to understand its dynamics first. Limiting our analysis to the first presentation of an input set revealed that outputs were already significantly decorrelated Fig. . This sh2 in Table input = 1) the output spiketrains are not well correlated (as shown by the mean spiketrain reliability Rw)  Fig. , consist41. We thus assessed such spike-wise noise directly from recording sets from our pattern separation experiments  was significantly higher in the original data , the set of output spiketrains would still be less similar than the set of inputs. First, the linear model describing the relationship between Rved Fig. . Moreoveved Fig. : this meved Fig. . We can Taken together, these results suggest that complex but noisy biophysical mechanisms allow GCs to balance temporal pattern separation and reliable signaling about their inputs.42, which would fit our conclusion that temporal pattern separation is dependent on neural noise (see paragraph above), (2) Short-term plasticity makes the probability of synaptic release dependent on the timing of the preceding input spikes, which could explain why temporal pattern separation and spiketrain reliability are not purely governed by noise. To test the idea that short-term synaptic dynamics is a potential mechanism controlling the pattern separation/spiketrain reliability ratio in GCs, we developed a computational model of a spiking GC with dynamic and probabilistic PP-GC synapses . The parameters of the model controlling the facilitation, depression and variability of presynaptic release, and thus the amplitude of EPSCs, were constrained to match the whole-cell voltage-clamp recording of a GC in response to a pattern separation protocol   Fig. . This is).To further understand the mechanisms behind temporal pattern separation in single hippocampal cells, we investigated the sources of the difference in decorrelation levels between the tested celltypes. We considered differences in intrinsic cellular properties Table , firing S9Both FS and HMC recordings displayed bursts of spikes (defined as more than one output spike between two input spikes), which was very rarely seen in our GC and CA3 recordings Figs , S8c,e. 6 which one could have expected from their reputation of having a much more reliable and precise spiking behavior than principal neurons43. The high fidelity in relaying input spike40 might still explain the difference in pattern separation ability between FSs and GCs, although, to our knowledge, they have never been formally compared. We thus first confirmed the idea that FS show much less spike-wise noise than GCs  and decorrelation performance in FSs , (2) although the correlation is better in FSs, FSs decorrelation levels are consistently lower than expected by the linear model fitting all celltypes , (3) There is no difference in terms of sparseness between CA3 PCs and their GC controls  in the DG network, where interneurons do not exhibit as much temporal pattern separation as the final DG output. In turn, the CA3 network seems to amplify this separation even more at the level of single PCs, suggesting that, at least in the hippocampus, it is not a computation specific to the DG.in vivo experiments that have difficulty identifying the cell-type of recorded units with certainty47 and simultaneously recording the direct inputs of these units17, in vitro brain slices that preserve the lamellar connections of the hippocampus offer a more accessible platform. For example, a similar experimental setup to ours was used to show that spatially segregated axonal inputs are represented by distinct spatiotemporal patterns in populations of DG neurons49. However, our study is the first to perform an experimental analysis of pattern separation within DG by directly manipulating the similarity of the inputs and comparing it to the similarity of simultaneously recorded outputs. Such a systematic approach had so far only been done in computational studies50. Although a rigorous comparison is impossible because the activity patterns considered were defined differently, the general pattern separation behavior of those models is confirmed by our experimental results: the DG itself performs a form of pattern separation, especially for input patterns that are highly similar  adaptive mechanisms, involving learning and recognition of input patterns, comparison with previously stored ones and the pruning out of common features, (2) non-adaptive (intrinsic) mechanisms, (3) or bothrns Fig. . We alsorns Fig. , a feat rns Fig. , suggestAdaptive or not, what is the biological source of the temporal decorrelation we observed? We first determined that intrinsic membrane properties do not predict decorrelation levels Table , and tha62 and both excitation and disynaptic inhibition from HMCs63. Thus, although both FSs and HMCs showed some temporal pattern separation , our results suggest that the final DG output is inherited from synaptic interactions between all DG celltypes, resulting in maximal separation at the level of GCs.Indeed, GCs are embedded in a network of synapses, notably receiving feedforward excitation from the PP, feedforward and feedback inhibition from FSs47. Our finding of pattern convergence in HMCs for dissimilar inputs may appear in contradiction with the recent reports that HMCs spatial representations remap in dissimilar environments47, but (1) activity patterns were defined differently and averaged over longer timescales (minutes) and (2) as argued above, remapping is not a direct measure of pattern separation without knowledge of the input patterns. Overall, we show that HMCs can exhibit both separation or convergence, depending on the time resolution and the amount of similarity between input patterns  compared to the similarity of the corresponding patterns of another network (e.g. DG)6. On the other hand, pattern completion is the process, happening over time, of retrieval of a previously learned full pattern in a network (e.g. CA3) from a partial seed pattern in the same network5. Our experiments did not test for pattern completion, as the different trains of an input set were not degraded versions of a previously learned pattern. Moreover, pattern separation and completion are complementary in the sense that pattern completion would benefit from initial input patterns being as separated as possible5. It thus makes sense that CA3 would start by amplifying the separation of input patterns, as is the case in our data, either before encoding or before proceeding to completion of the seed pattern. In fact, our results suggest that CA3 might complement the separation effectuated in the DG, as CA3 is able to perform high decorrelation levels at long timescales (>100 ms) when the DG does not  and regularly monitored and approved by the University of Wisconsin Institutional Animal Care and Use Committee.72). The cutting solutions contained : 83/80 NaCl, 26/24 NaHCO3, 2.5/2.5 KCl, 1/1.25 NaH2PO4, 0.5/0.5 CaCl2, 3.3/4 MgCl2, 22/25 D-Glucose, 72/75 Sucrose, 0/1 Na-L-Ascorbate, 0/3 Na-Pyruvate, bubbled with 95% O2 and 5% CO2. During the dissection, brain hemispheres were prepared following the “magic cut” procedure73 with α-angle around 10 to 15° and β-angle around 5 to 10° for GC and FS recordings, and with α close to 0° and β between 0 and −5° for HMC and CA3 pyramidal cells recordings66. Slices were cut using a vibratome (Leica VT1000S) then placed in an incubation chamber in standard artificial cerebrospinal fluid (aCSF) containing (in mM) 125 NaCl, 25 NaHCO3, 2.5 KCl, 1.25 NaH2PO4, 2 CaCl2, 1 MgCl2, and 25 D-Glucose  at 35 °C, for 15–30 minutes after dissection. Slices were stored in the incubation chamber at room temperature for at least 30 minutes before being used for recordings.Horizontal slices of the ventral and intermediate hippocampus (400 μm) were prepared from the brains of young (p15–25) or adult (p121 ± 15 days) C57BL/6 male mice (Harlan/Envigo). Adult animals were only used for Fig. 2ATP, 0.3 NaGTP. For the dataset from adult mice, we used a slightly different recipe: 135 K-gluconate, 5 KCl, 0.1 EGTA, 10 HEPES, 20 Na-Phosphocreatine, 2 Mg2-ATP, 0.3 Na-GTP, 0.25 CaCl2. Intracellular solutions were adjusted to pH 7.3 and 310 mOsm with KOH and H2O. Both recipes yielded similar electrophysiological behaviors.All recordings were done in standard aCSF adjusted to 325 mOsm, at physiological temperature (33–35 °C). Whole cell patch-clamp recordings were made using an upright microscope  with infra-red differential interference contrast optics. Patch pipettes pulled from thin-walled borosilicate glass  had a resistance of 2–5 MΩ when filled with intracellular solution containing (in mM) 140 K-gluconate, 10 EGTA, 10 HEPES, 20 phosphocreatine, 2 MgRecordings were done using one or two Axopatch 200B amplifiers , filtered at 5 kHz using a 4-pole Bessel filter and digitized at 10 kHz using a Digidata 1320 A analog-digital interface (Axon Instruments). Data were acquired to a Macintosh G4  using Axograph X v1.0.7 (AxographX.com). Stimulation pipettes were pulled from double barrel borosillicate theta-glass  and filled with ACSF or a 1 M NaCl solution and connected to a constant current stimulus isolator used to generate 0.1–10 mA pulses, 100 microseconds in duration.Series resistance and cellular intrinsic properties were assessed online in Axograph from the fit of the electrical responses to repetitions of 5–10 mV, 25 ms steps, holding the potential at −65 mV. Neurons used for analysis were stable across a whole recording session as judged by monitoring of series resistance and resting potential.rest) −69.3 ± 1.3 mV; input resistance (Ri) 171 ± 16 MΩ and capacitance (Cm) 23 ± 2 pF. GCs from adult mice had the following intrinsic properties: Vrest −78.8 ± 1.8 mV; Ri = 137 ± 14 MΩ and Cm = 18 ± 1 pF.Dentate granule cells (GC) were visually identified as small cells in the granule cell layer (GCL). GCs from young mice had the following intrinsic properties (mean ± sem): resting potential  were identified as neurons with large somata at the hilus-GCL border and a high firing rate response during large depolarizing current steps, and a large after-hyperpolarization (AHP)74 Fig. . They ha75  were identified as large neurons in the deep hilus (>60 µm away from the GCL) with a regular firing response to current steps and a small AHP, as well as with a high frequency of spontaneous EPSPs, all characteristics that allow to distinguish them from other neurons in the hilus75 Fig. . Their i71, we avoided the extremity of the CA3c region and targeted large neurons around the CA3b/c border in the pyramidal layer. Their intrinsic properties under partial inhibitory block (aCSF + 100 nM gabazine) were: Vrest = −72.7 ± 2.2 mV; Ri = 186 ± 12 MΩ and Cm = 36 ± 2 pF. The intrinsic properties of GCs recorded under the same pharmacological conditions were: Vrest = −77.3 ± 1.9 mV; Ri = 244 ± 13 MΩ and Cm = 25 ± 2 pF.For CA3 pyramidal cells, because recent studies suggest a proximodistal gradient of physiological and computational properties in the CA3 networkinput) computed with a binning window (τw) of 10 ms, using two different algorithms . An input set consisted of five different input spiketrains, each 2 s trains of impulses simulating cortical spiketrains, with interspike intervals following a Poisson distribution. Each pair of input trains had a correlation coefficient close to the Rinput of its set . For all experiments except in Fig. 52, and is known to promote a high probability of spiking in GCs in slices77.We designed multiple sets of input patterns, each with a prespecified average Pearson’s correlation coefficient . Stimulus current intensity and location were set so that the recorded neuron spiked occasionally in response to electrical impulses  but consistent with sparse activity generally observed in GCs in vivo during behavior78 and in slices under conditions of drive comparable to ours80. The output firing rates of FS and HMC were higher than their GC controls  the stimulating electrode was placed in the inferior blade of the GCL to make the DG output cells fire, (2) we targeted CA3b PCs, which have the highest E/I ratio across CA3 in slices and still receive strong connections from mossy fibers32, (3) we used input sets with 30 Hz input trains to promote the depression of inhibitory transmission31, (4) we maintained the membrane potential baseline between −60 and −70 mV, and (5) we had to add 100 nM gabazine to the bath of standard aCSF in order to slightly decrease IPSCs amplitude . 100 nM of gabazine  has been shown to correspond to a 70% availability of GABA-A receptors81 and, in our conditions, consistently decreased spontaneous IPSCs mean amplitude by 30% in voltage-clamp recordings of GCs and CA3 PCs held at −40 mV. These conditions allowed us to record for the first time the spiking output of CA3 PCs in response to complex input spiketrains while preserving some inhibition in the network31. PCs output firing rate were on average below 10 Hz , we had to change several parameters in order to make CA3 PCs spike. Indeed, PCs firing is controlled by strong feedforward inhibitionw timescale. Sweeps without spikes were excluded from further analysis.For each recording set, the similarity between pairs of spiketrains was computed as the Pearson’s correlation coefficient between the spiketrains rasters binned at a τfiring rates, probability of bursting and spike-wise noise parameters , but computed them directly for each recording set of spiketrains from a pattern separation experiment. The mean firing rate was computed as the average firing rate across all fifty output spiketrains. A burst was defined as the occurrence of more than one output spike in the interval of two input spikes , where µ is the mean delay of an output spike and σ is the jitter of this delay. The baseline corresponds to the background firing, occurring by chance or caused by neighboring inputs. After subtracting the baseline and extracting the probability of spiking by dividing the counts of output spikes by the total number of input spikes, we defined the spike probability (SP) as the sum of probabilities of an output spike in the predefined time interval around an input spike , where Nspk is the number of input spikes in a sweep, and F the probability of not spiking (F = 1 − mean SP = 1 − 0.42). A random delay, sampled from a Gaussian distribution N, was added to each resulting spike times, with µ and σ being respectively the mean delay and mean jitter in the original recordings. The noise statistics of the resulting simulated data set is shown in Fig. To assess the role of spike-wise neural noise in pattern separation, we generated two data sets. First, we simulated output spiketrains in response to our input sets . This simulation was entirely based on the average spike-wise noise parameters computed from the original GC recordings (see above): the matrix of input spike times was replicated ten times, and for each of the fifty resulting sweeps, spikes were deleted randomly following a binomial distribution 82 with an adapted Tsodyks-Markram (TM) synapse model83 designed to capture short-term plasticity of stochastic neurotransmission at the LPP-GC synapse. The TM synapse model consists of a system of two ordinary differential equations describing the dynamics of X and U. X is the probability of a presynaptic vesicle being available for release, the decrease of which leads to synaptic depression, and U is the probability of release for an available vesicle, the increase of which models facilitation:rec is the time constant of vesicle recovery after release, τfacil is the decay of U controlling synaptic facilitation, USE a factor determining the probability of release Pr at the time of the first spike, δ the Dirac delta function, and tAP the time of arrival of an input spike. The system of ODEs was solved using the ode23 MATLAB solver.To test whether probabilistic synaptic dynamics is a potential mechanism of temporal pattern separation, we used an Izhikevich model of a regular spiking neuronk vesicles are released, drawn from a binomial distribution B, where N is the number of release sites and Pr is the probability of release defined as the product of X and U. The current I is then the product of k with the quantal size q. Based on previous data from our lab, we considered q = 20 pA62. The version of the TM model we implemented only focuses on the dynamics of the presynaptic vesicles, ignoring postsynaptic dynamics. To match the decay of real EPSCs recorded at the soma, I was convolved with a template EPSC shape:rise = 0.5 ms, τdecay = 5 ms and A is a factor normalizing the area of the resulting shape to 10. In addition, a synaptic delay of 4 ms was introduced. The resulting current  = −70 mV and x(0) = b.V(0).Based on the quantal theory of neurotransmission, the model assumes that, at each time point t, ent Fig.  is then input = 0.76) and compared to the current dynamics and spiking behavior of a representative GC that was recorded both in voltage-clamp and current-clamp in response to the same input set. For the TM model, the following parameters were adjusted to match the dynamics and the peak of the current averaged over ten repetitions, as well as the variability in the peak amplitude of the first EPSC: τrec = 500 ms, τfacil = 9 ms, USE = 13, N = 40 release sites. Finally, because the Izhikevich model integrates non-dimensional currents, the input current had to be scaled using the dividing constant K = 62, such that the average firing rate of the simulation matched the average firing rate of the representative GC to the same input set (3.5 Hz). K can be considered as a constant modelling a tonus of inhibition: the higher K is, the more difficult it is for an EPSC to make the Izhikevich neuron fire. In the end, the standard deviation of the firing rate was ~0.6 Hz for simulations, compared to 0.8 Hz for the original recording. The only source of variability was from presynaptic dynamics of vesicle release modelled as a binomial process.To optimize the non-fixed parameters of the TM-Izhikevich model, we simulated output spiketrains in response to ten repetitions of a single input set  were chosen based on estimates from the literature on mossy fiber buttons, the giant GC-CA3 PCs synapses44. τrec, τfacil and K were adjusted to model short-term synaptic facilitation observed at mossy fiber buttons and to match the average firing rate that we observed in our CA3 current-clamp recordings made under gabazine , including functions from toolboxes cited above. Sample sizes were chosen based on the literature and estimations of the variance and effect size from preliminary data. All values are reported as mean ± S.E.M. unless otherwise noted. The one-sample Kolmogorov-Smirnov test was used to verify the normality of data distributions. Parametric or non-parametric statistical tests were appropriately used to assess significance . Assumptions on equal variances between groups were avoided when necessary. All T and U tests were two-tailed. To determine whether two distributions of data points are significantly different (e.g. Ra, includSupplements


Polymeric amines and complex amine containing system are actively studied and applied as gene delivery agents in gene therapy and genetic engineering. Optimizing polymer – nucleic acid ratio is the key stage in elaboration of procedures in this area. Application of fluorescent tagged oligonucleotides is widespread approach which allows to visualize nucleic acid in gel electrophoresis experiments and to find conditions of the full binding of the nucleic acid. We suggest to use succinimidyl ester of 7-(diethylamino)coumarin-3-carboxylic acid as an agent for fluorescent labeling of polymeric amines and to use the tagged polymers in optimizing polymer – nucleic acid ratio. This approach allows to see unbound polymer and to study various nucleic acids in interaction with the same polymer.•Labeling of gene delivery agents with fluorescence groups increases efficiency of optimization of gene delivery compositions.•Polymeric amines tagged with succinimidyl ester of 7-(diethylamino)coumarin-3-carboxylic acid are suitable for study polymer – nucleic acid interaction with gel electrophoresis. Specifications TableStudy of the interaction between nucleic acids  and synthetic polymers is important in elaboration of new methods in genetic engineering and in gene therapy. Gel electrophoresis is a convenient method for detection of this interaction and for estimation of optimal nucleic acid – polymer ratio. Fluorescence dyes are usually applied to visualize the electrophoresis results. There are two main ways to introduce fluorescence agent in to electrophoretic system: to use intercalation agents like ethidium bromide  or to taSuccinimidyl ester of 7-(diethylamino)coumarin-3-carboxylic acid (SECCA) was recommended as a tool for introducing fluorescent coumarin groups into aminoacids and proteins . In thisThe initial succinimidyl ester of 7-diethylaminocoumarin-3-carboxilic acid (SECCA) was prepared according to . Polyeth3OH:CH2Cl2: 25% aq. NH3 = 2:1:1). At the end of the process turbidity of the mixture became much lower. The solution was filtered through a 1 cm layer of alumina oxide. The solvent was evaporated under reduced pressure, the residue was mixed with 5 mL of distilled water and acidified to pH 1–2 with concentrated hydrochloric acid and dialyzed in Biotech CE Tubing (MWCO: 500–1000) against distilled water during seven days. The degree of purification of the polymer from the dye was checked by TLC illuminated with a 395 nm flashlight and with gel electrophoresis  in 2 mL of deionized water glacial acetic acid  was added. pH of the solution was adjusted to 7.5 with gradual addition of 1.175 mL of 1 M NaOH. After that a solution of SECCA in DMF  was added to the clear polymer solution. The resulted turbid mixture was shaken for 4 h at room temperature (23 °C) and left for 18 h in a dark place. The course of the reaction was checked with TLC on silica gel plates illuminated with a 395 nm flashlight  was fluorescently tagged with SECCA analogously to PVA. The amounts of reagents were proportionally increased.−1 M−1 at 409 nm). UV–vis absorption measurements were carried out using a Cintra 20 UV/vis spectrophotometer . The modified PVA contains 1.6 mol % of the fluorescent units and PEI – 0.85 mol %.Fluorescent labeling of polymer was quantified according to the Beer-Bouguer-Lambert’ law and taking the molar extinction coefficients of 7-diethylaminocoumarin-3-carboxilic acid as standard  . Electrophoresis experiments were performed with a Mini-Sub (7 × 10 cm) Cell GT System  with an ELF-4 power supply (DNA-Technology LLC) and TCP-20.LC transilluminator (Vilber Lourmat), operated at 254 nm. The gel running buffer was 40 mM Tris acetate (pH adjusted to pH 7.4) and 1 mM ethylenediaminetetraacetic acid (EDTA). A glycerol gel loading buffer was applied , 50% glycerol for 10x reagent).DNA and polymer solutions were mixed according to Thus, modification of polymeric amines with succinimidyl ester of 7-diethylaminocoumarin-3-carboxilic acid is a convenient method to study interaction between polymer and nucleic acids. Behavior of the tagged polymers during gel electrophoresis is easy visualized by fluorescence and this allows to optimize polymer – nucleic acid ratio in gene delivery and gene engineering experiments.


Although the brown hare population originating from agricultural area displayed a slight excess of heterozygous individuals 2a2b (F = − 0.04), it was conformed to the Hardy–Weinberg assumption . Compared with this population, a sevenfold reduced frequency of the phenotype 2b and above tenfold increase of a heterozygosity (F = − 0.53) was observed in the brown hare population inhabiting the vicinity of metallurgical plant. Therefore, this population did not fit to the Hardy–Weinberg law . Despite the negligible genetic differentiation (FST = 0.026) between brown hare populations inhabiting areas with different anthropogenic pressure, a statistically significant difference in the distribution of their phenotypes  and alleles  was noted. The collected data confirm that the brown hare species is sensitive for environmental quality and may serve as a good indicator of habitat conditions related to both organic pollution emitted by agricultural activities (PIC = 0.48) and inorganic contamination originating from metallurgical processes (PIC = 0.49). These difference in the environmental quality might be assessed by estimation of genetic variability among the brown hare populations, based on the phenotypes distribution of histone H1 variant H1.2, the protein that was not so far employed as a molecular marker of anthropogenic stress.From the liver tissues of brown hare individuals that lived in two various habitats, i.e., the agricultural region with the predominant farms and the industrial area near a metallurgical plant, histones H1 were analyzed to compare their within and between population variability. Furthermore, because agricultural production emits mainly organic pollutants and metallurgical industry is a primarily source of inorganic contaminations, we wanted to check how the brown hare individuals are sensitive for both agents. Among brown hare H1 histones, the histone H1.2 was determined as heterogeneous due to its varied mobility in two-dimensional SDS–polyacrylamide gel. The obtained electrophoretic patterns contained differently moving single spots of histone H1.2 and also its double spots have a similar rate of electrophoretic mobility. Based on this, two homozygous phenotypes (slowly migrating 2a and faster moving 2b) and a heterozygous phenotype (2a2b) was distinguished. The relatively low variable (CV < 0.25) and comparably abundant ( Both fiwww.rsbweb.nih.gov/ij). A raw integrated density, which indicate a sum of the values of the pixels in the selected gel area, was measured to evaluate an abundance of histone H1.2 phenotypes protein spots. The measurements were repeated for the seven preparations (n = 7) of each histone H1.2 phenotype.The obtained electrophoretic patterns of H1 histones were recorded with the gel imaging system Doc-Print II (Vilber Lourmat) and processed by the software ImageJ 1.44c  test of goodness-of-fit. A difference among the brown hare age and sex as well as between the levels of histone H1.2 alleles and phenotypes was evaluated with the use of Chi square (χ2) test of homogeneity. In all tests, a p value <0.05 was regarded as statistically significant for rejecting the null hypothesis. The genetic differentiation of brown hare population was evaluated by Wright’s F statistics indices  and a comparable share in the heterozygous phenotype (Table p = 0.415). Based on the calculated values of the coefficient of variation, not exceeding 0.25, a relative variability of histone H1.2 phenotypes is low (Table 2a and 2b) at a locus.Histone H1 preparations obtained from brown hare livers in the perchloric acid soluble fraction were initially analyzed in the first dimension acetic acid urea polyacrylamide gel. As seen in the Fig. Χ2= 3.54, p = 0.17) as well as between both groups of sexes  has been detected in the brown hares originating from all regions in which they were caught , a population living in the vicinity of metallurgical plant in the Nowa Huta significantly differ from the Hardy–Weinberg expectation . A severe shortage of the homozygous 2b individuals (frequency 0.05) was detected in the amount of seven times less compared with the same individuals from the area of Wopławki (frequency 0.35). Besides, the surplus of heterozygous 2a2b individuals, exemplified by a negative value of within population inbreeding coefficient  and phenotypes  frequency was found between populations. To determine differentiation of brown hare populations, the Wright’s F-statistics was used as a measure of their genetic variation. The different values of individuals (HI = 0.625) and subpopulations (HS = 0.485) heterozygosity confirmed deviation from the Hardy–Weinberg assumption, which also was identified in relation to the negative value of FIS (− 0.288) and FIT (− 0.255) indices calculated from observed and expected heterozygosity, respectively  and petrochemical contamination  (Damek-Poprawa Apodemus flavicollis)  and bank vole (M. glareolus), respectively, the data presented by Mussali-Galante et al. (Peromyscus melanophrys). The brown hare species is sensitive to many factors that influence a density of its population. The landscape change corresponding to expanded urbanization and agriculture intensification (Edwards et al. The environmental stress associated with habitat pollution can affect population structure by increasing and/or decreasing its genetic variability. As a consequence, a shift in the distribution of tolerant phenotypes and correlation between the contaminants and allele frequency may occur. The susceptibility to pollutants reduces a viability of organisms and weakens the survival of a species (Van Straalen and Timmermans e et al.  indicateIn summary, the results presented in this work show that the polymorphic histone H1.2 is informative in the monitoring of genetic variations in the brown hare population, which undergo a rearrangement in relation to anthropogenic changes correlated with different environmental pollutants.


Soil blocks were also sampled at 6–15 cm depth to determine basic soil properties and to measure soil aggregate strength parameters. The range of soil water contents appropriate for tillage were determined using the water retention and the consistency approaches. SOC content in Highfield was in the order: G > LA = A > BF, and in Askov: 1½ AM > 1NPK = ½NPK > UNF. Results showed that different long-term management of the silt loam Highfield soil, and fertilization of the sandy loam Askov soil affected the mechanical properties of the soils— for Highfield soil, aggregates from the G treatment were stronger in terms of rupture energy when wet  than the BF treatment. As the soil dried , soil aggregates from the G treatment were relatively weaker and more elastic than the BF soil. Our study showed, for both Highfield and Askov soils, a strong positive linear increase in the range of water contents for tillage with increasing contents of SOC. This suggests that management practices leading to increased SOC can improve soil workability by increasing the range of water contents for tillage. We recommended using the consistency approach over the water retention approach for determining the range of water contents for tillage because it seems to give realistic estimates of the water contents for tillage.The effects of soil organic matter on the water contents for tillage were investigated by sampling soils with a uniform texture, but a range of soil organic carbon (SOC) from two long-term field experiments at Highfield in Rothamsted Research, UK and Askov Experimental Station, Denmark. The treatments studied in Highfield were Bare fallow (BF), Continuous arable rotation (A), Ley-arable (LA) and Grass (G); and in Askov: unfertilized (UNF), ½ mineral fertilizer (½ NPK), 1 mineral fertilizer (1NPK), and 1½ animal manure (1½AM). Minimally disturbed soil cores (100 cm One of the primary purposes of tillage is for seedbed preparation, where operations are designed to alter soil bulk density, aggregate size distribution and other soil physical characteristics to create soil conditions and environment favoring crop establishment, germination and growth .θRANGE) where soil is workable. In this study, soil workability is defined as the ease of working with a well-drained soil to produce desirable seedbeds (θRANGE is the difference between the wet tillage limit (θWTL) and the dry tillage limit (θDTL). θWTL and θDTL are the upper and lower water contents for tillage, respectively. Optimum water content for tillage (θOPT) is the water content where tillage produces maximum number of smaller fragments and minimum number of large fragments (clods)  . Russell (clods)  and the  (clods) .θWTL and θDTL and the effects of soil physical properties on these limits are crucial. Such knowledge can provide practical information on the satisfactory ΔθRANGE over which tillage operations produce desirable soil structures for crop establishment and growth  is a critical soil property that affects many other soil physical properties and functions. Organic binding agents such as roots and fungal hyphae play an important role in soil aggregation and stabilization , and imps higher , which mThe objectives of this study were to: (i) quantify the effect of SOC on the mechanical behavior of soil aggregates and the water contents for tillage, and (ii) evaluate the water retention and consistency approaches for determining the range of water contents for tillage. We hypothesized that the range of water contents for tillage increases with increasing SOC content.22.1Soil samples were taken from two long-term field experiments; the Highfield long-term, ley/arable experiment at Rothamsted Research, UK  and from the Askov long-term experiment on animal manure and mineral fertilizers at Askov Experimental Station, Denmark . These soils had uniform textures, but a range of SOC.The soil from Highfield is a silt loam classified as Chromic Luvisol according to the World Reference Base (WRB) soil classification system . The expTriticum aestivum L.), silage maize (Zea mays L.), spring barley (Hordeum vulgare L.), and a grass-clover mixture  used for cutting in the following year  were taken from 6 to 10 cm depth by inserting steel cylinders gently into the soil. Six soil cores were sampled per plot at both locations. In addition, soil blocks were sampled at 6–15 cm depth: Two soil blocks (4000 cm3) per plot in Askov, and three blocks (2750 cm3) per plot in Highfield. The soil cores were stored in a field moist condition in a 2 °C room until analysis. Portions of the soil blocks per plot were spread out on a table and carefully fragmented by hand along natural planes of weakness and left to dry in a ventilated room ∼20 °C.At Askov, sampling took place in September 2014 following a winter wheat crop. At Highfield, sampling was done in March 2015. At both Askov and Highfield, soil cores . Soil texture was determined on portions of the <2 mm samples using a combined hydrometer/sieving method after removal of soil organic matter by hydrogen peroxide .2.4d is equivalent cylindrical pore diameter (μm) and ψ is the soil matric potential (hPa).To obtain water retention curves, water content was measured from the six soil cores per plot from Askov at −10, −30, −100 and −300 hPa matric potentials; and at −10, −30, −100, −300 and −1000 hPa matric potentials for Highfield soil on tension tables, vacuum pots and pressure plates . Water c2.5Plastic limit (PL) was determined using the standard ASTM (Casagrande) test procedure . In brie2.6The water contents for tillage were determined using two approaches: (i) water retention approach, and (ii) consistency approach.2.6.1θ, kg kg−1) corresponding to each matric potential (hPa) was calculated by fitting the van Genuchten equation with the m = 1-1/n to each set of water retention data obtained from each plot at Askov and Highfield:ATθs and RESθ are the water contents at saturation, i.e. at h = 0, and the residual water contents, h = ∞, respectively, α is a scaling factor for h; and n is a fitted parameter that controls the shape of the curve. RESθ was set equal to zero. Values of n were obtained using the curve-fitting program, RETC  was estimated as follows:The wet tillage limit (θOPT) was estimated as water content at the inflection point of the soil water retention curve (θINFL):The optimum water content for tillage (DTL) was estimated as proposed by The matric potential at the dry tillage limit (hθDTL) was calculated by putting the value of hDTL from Eq. (The corresponding water content at the dry tillage limit (from Eq.  into 2)θDTL) wasθRANGE (water retention)) was calculated as:The range of water contents for tillage using the water retention approach  of 8–16 mm soil aggregates and gravimetric water content measured at different matric potentials ) was calculated as described by The range of water contents for tillage using the consistency approach  and fieY was calculated from the equation suggested by F is the maximum force (N) at failure and d is the effective diameter of the spherical aggregate (m); it was obtained by adjusting the aggregate diameter according to the individual masses , 0m is the mass (g) of the individual aggregate and 1m is the mean mass of a batch of aggregates of the same size class (in this case 15 aggregates for each size fractions).For both Highfield and Askov soils, l masses :(12)d = rE) was calculated from the area under the stress-strain curve up the point of tensile failure (i)F was defined on gravimetric basis from the equation:m is the mass of the individual aggregates.Rupture energy ( failure :(13)Er≈ΣE) was determined to obtain a quantitative measure of stiffness (elasticity) of the aggregates (determined only for the Highfield samples). It was estimated from the gradient of the stress-strain curve to the elastic limit, assuming linearity up to that point, which was determined using a macro program:σ is stress (Pa) and ԑ is strain.Young's modulus  to yield normal distribution. The Highfield data were fitted to a linear mixed effect model, which comprised treatment as fixed and block as random factors. The Kenward-Roger method was used to calculate degrees of freedom. For the Askov data, treatment effects were analyzed using a linear model which comprised block as a fixed effect. We used p < 0.05 as a criterion for statistical significance of treatment effects. Where effect of treatment was found to be significant, further analyses were made to identify which treatment means were different (pairwise comparison) using the general linear hypotheses (glht) function implemented in R multcomp package. For the four BF replicates which were not included in the original randomized block design, a paired t-test was used to investigate if the treatment significantly differed from the A, LA and G treatments. We acknowledged that the paired t-test statistics performed to compare statistical significance difference between the BF treatment and the A, LA and G treatment was a less robust test. Throughout the presentation of Results (Section t-test are shown by an asterisk (*) symbol against the A, LA or G treatment.All statistical analyses were carried out in R software package . The Y,  Section , statist33.1θPL was lower for the BF treatment than the other treatments at Highfield . The same was also seen at Askov, although not significant .Soil bulk density was significantly greater for the BF and A soils than the LA and G treatments, and for the UNF and ½NPK compared to the 1NPK and 1½AM treatments . There wighfield . θPL inc3.2Y and spE values for all the aggregate size fractions measured did not differ between the treatments  compared to the A and BF treatments . Aggregates for the size fraction 2–4 mm were more elastic for the G treatment than the A and LA treatments, whereas for 4–8 mm size fraction, the LA treatment was more elastic compared to both the A and G treatments. Geometric mean values of all size fractions showed that the G and LA treatments had lower E (high elasticity) compared to the BF treatment , Y values did not differ significantly between treatments, whereas at moist–dry state , aggregates for the LA and G soils had lower Y compared to the A treatment  were related to SOC content. There was a negative linear decrease in Y with increasing SOC content (p < 0.05). A stronger negative linear relationship was found between SOC and E (p < 0.001). In contrast, there was a positive linear increase in spE with increasing SOC content, although not significant (p = 0.07)  a–c. Overd by SOC a–c.Fig. 3.5θDTL) for each plot was graphically determined from the relationship between Y of aggregates in the 8–16 mm size range and the gravimetric water content at −100, −300, −1000 hPa matric potentials and at air-dry state. Examples of how we determined water content at twice the strength at θOPT for the BF and G soils from Highfield, and the UNF and 1½AM soils from Askov are presented in θDTL for the BF soil was 0.16 kg kg−1 and 0.22 kg kg−1 for the G soil. θDTL for the UNF and 1½AM soil were 0.09 and 0.10 kg kg−1, respectively.Water content at dry tillage limit (θRANGE (water retention) and ΔθRANGE (consistency) are presented in θDTL, θOPT, θWTL at treatment levels are also shown for the two approaches. The G treatment with high SOC content had wider ΔθRANGE compared to the BF treatment at Highfield; and for the 1½AM compared to the UNF at Askov. Based on the water retention approach, ΔθRANGE for the G and BF treatments were 0.18 and 0.06 kg kg−1, respectively (−1 for the 1½AM and UNF treatments (θRANGE (consistency) for the G treatment was 0.11 kg kg−1 compared to 0.03 kg kg−1 for the BF treatment, and 0.06 kg kg−1 for the ½AM treatment compared to 0.05 kg kg−1 for the UNF treatment  was more significant and more of the variation was explained (θRANGE (water retention) (SOC content had a highly significant positive effect on ΔθRANGE a–d. The xplained b and d ttention) a and c.F44.1Y a potentially sensitive measure of soil structural condition. Results showed that SOC had a negatively and a significant effect on geometric mean of Y across the four aggregate size classes when air-dry  for the sandy loam soil at Askov. Results here suggest that the range of water content for measurement of Y is important to study the effect of SOC on soil aggregate strength.The indirect tension test causes soil aggregates (or cores) to fail along pre-existing failure zones, and planes of weakness making  air-dry a. This cd by SOC . Studiestentials . Howeverectively . Our resY, spE does not involve any assumption of the mode of failure, making it more appropriate for estimating the strength of dry aggregates. spE is also appropriate for estimating the strength and fragmentation of wet aggregates. We observed that at −100 hPa matric potential, spE was significantly greater for the G compared to the other treatments at Highfield. This could be ascribed to the influence of SOC including organic binding and bonding materials such as polysaccharides fungal hyphaes and roots . Our results showed that geometric mean of spE across the four aggregate size classes in air-dry state increased with increasing SOC content, although the relatioship was weak  soil (0.09 kg kg−1) than the UNF soil (0.06 kg kg−1).The wider ΔθRANGE (consistency) for the G soil at Highfield (0.11 kg kg−1) compared to what was reported by θRANGE showed that an increase in SOC content could potentially improves the window of opportunity for tillage operations by increasing ΔθRANGE over which tillage can be satisfactorily executed. θWTL, θOPT, θDTL and ΔθRANGE. Moreover, SOC influences the plastic behavior of soil by shifting the plastic limit to greater water content , whereas it was very wet  for the BF soil  destroying the soil structure and therefore, does not represent soils with intact structure. Moreover, plastic limit (PL) does not take into consideration pre-existing cracks which are important in soil fragmentation  avoid producing soil seedbed dominated by large smeared fragments during tillage, which are of less agronomic value in terms of crop establishment (Knowledge of the water contents (wet and dry limits) for tillage is useful for determining the range of water contents over which soil is workable, i.e., tillage can be performed satisfactorily. In temperate regions like Northern Europe, where soil workability is likely to be limited by excessive moisture, information on lishment ; and (2)θDTL is also useful to: (1) avoid soil pulverization during tillage because seedbeds become dominated by both large intractable clods and very fine particles (dust) leading to poor aeration, vulnerability to crusting and greater erodibility dibility .θRANGE. This information can provide practical evidence to farmers to engage in farm management practices that improve SOC as a way of widening the window of opportunity over which tillage can be performed satisfactorily.It should be emphasized that for practical purposes before the application of our results in a DSS, it is important that the more promising consistency approach for determining the range of water contents for tillage, is validated under field conditions. Also, more knowledge is needed on the effect of SOC on different soil types and at different scales. It should also be pointed out that the high values of SOC associated with the G treatment may be due in part to the fact that it has not been cultivated. Cultivating it would lead to a sharp drop in SOC over time. However, the scope of this study could be expanded to identify appropriate conditions for grazing without risk of damage (poaching) to the underlying soil structure.5•The aggregates from the Grass (G) treatment with large SOC content were stronger based on the mass specific rupture energy when soil was wet than the Bare fallow (BF) soil with small SOC content.•Aggregate tensile strength for the G treatment was significantly lower than the Arable (A) and BF, and more elastic than the BF, A and Ley-arable (LA) treatments when soil was moist.•The soil consistency approach provided more reliable estimates of tillage limits  than the water retention approach.•θRANGE) —SOC explains 78 and 87% of the variation in ΔθRANGE for the studied soils.Management practices leading to increased SOC content can improve soil workability by increasing the range of soil water contents suitable for tillage (ΔThis study showed that the different long-term management practices on two contrasting soils lead to differences in soil organic carbon (SOC). This in turn led to major differences in soil mechanical properties  which are useful in identifying appropriate soil moisture conditions for tillage. Two approaches were used to identify the range of soil water contents for tillage: (i) Based on fixed points (water contents) generated from modeled water retention characteristics and (ii) based on a combination of soil consistency relationships (plastic limit) and an estimate of tensile strength of aggregates in the 8–16 mm size class. The evidence here suggests:None.


Here we report surface quantum oscillation and its evolution into quantum Hall states in Cd3As2 thin film samples, where bulk dimensionality, Fermi energy, and band topology are systematically controlled. We reveal essential involvement of bulk states in the quantized surface transport and the resultant quantum Hall degeneracy depending on the bulk occupation. Our demonstration of surface transport controlled in film samples also paves a way for engineering Fermi-arc-mediated transport in topological semimetals.Unconventional surface states protected by non-trivial bulk orders are sources of various exotic quantum transport in topological materials. One prominent example is the unique magnetic orbit, so-called Weyl orbit, in topological semimetals where two spatially separated surface Fermi-arcs are interconnected across the bulk. The recent observation of quantum Hall states in Dirac semimetal Cd 3As2 is possibly evolved from topological states. Here, Nishihaya et al. report surface quantum oscillation and its evolution into quantum Hall states in Cd3As2 thin films, where unconventional bulk states involve in quantized surface transport.The recent observation of quantum Hall states in bulk Dirac semimetal Cd Being gapless types of topological materials, Dirac and Weyl semimetals (DSM/WSM) show unique open-arc surface states (Fermi-arcs) as a direct consequence of the monopole charges ±1 carried by the paired band crossing points (Weyl points)4 , where two Fermi-arcs on opposite surfaces are interconnected by bulk chiral modes (N = 0 Landau levels) transferring electrons across the slab thickness5 4 Fig. . One cruss5 Fig. , leading10 and the following experimental observation of quantum Hall (QH) states in bulk nano-plates of the DSM Cd3As212 has provoked further interest in the novel quantization physics in three-dimensional (3D) topological semimetals. The actual underlying mechanism for the quantized transport, however, has remained elusive and controversial. In a DSM consisting of two copies of WSM overlapping with opposite chirality, possible formation of topological-insulator-like surface orbits localized on each sample surface  gapped energy structure is necessary. In this context, it is highly meaningful to adopt thin film samples with excellent tunability of dimensionality, Fermi level (EF), and interfaces to investigate the bulk contribution to the surface QH states in DSM.The recent theoretical prediction of Weyl orbit quantizationace Fig.  has also3As2 films. By controlling the topological phase transition with Zn doping, we have clarified that the quantized conduction originates from the surface state of the DSM phase. The possible confusion between the surface QH state and the conventional QH state induced by confined bulk states14 is avoided by designing an appropriate film thickness. Through careful analysis of the surface QH states particularly with electrostatic control of EF, we reveal the essential involvement of the bulk states and the QH degeneracy highly dependent on the bulk occupation.Here, we report 2D quantum oscillations and QH states observed in 3D DSM Cdx1−Znx)3As2 (112) oriented films were measured for different current I and magnetic field B configurations. First, we present in Fig. B ⊥ I, θ = 90°) and longitudinal MRs  of a 100 nm thick Cd3As2 film measured with a pulsed field up to 55 T. Focusing on the quantum oscillations, the longitudinal MR is characterized by a single oscillation frequency corresponding to the 3D bulk Fermi surface. In the transverse MR, on the other hand, another oscillation component with a higher frequency (a larger Fermi surface area FS) is superimposed on that of the bulk state , suggesting that the additional conduction component in the transverse MR is of 2D nature developed from the surface states.Magnetoresistances (MRs) of , in good agreement with previous reports on those of Cd3As2 bulks17. By contrast, m* becomes 5–6 times larger for the higher-frequency oscillations observed above 20 T. The quicker decay of the 2D quantum oscillations as compared to the bulk oscillations against temperature rise is consistent with the surface transport reported for (112) bulk nano-plates in previous studies8. The difference in the underlying band dispersions between the bulk and the 2D transport can also be found in the indices of Landau levels (LLs). While the bulk state reaches N = 2 already at 30 T, the QH plateau indicates much larger filling factors ν such as ν = 15, 13, and 12, which cannot be explained by the bulk LLs.Figure x = 0.14, 0.17, and 0.21) . To explicitly display the differences in the oscillation components in the transverse and longitudinal MRs, their second derivatives are also presented in Fig. x = 0.14 and 0.17, an additional oscillation component of a higher frequency coexists with the bulk oscillations in the transverse MR similarly to the non-doped case presented in Fig. x = 0.21, on the other hand, only a single oscillation component dominates, exhibiting no sign for the existence of an additional Fermi surface. The chemical doping of Zn reduces the spin–orbit coupling and lifts the band inversion in Cd3As2, inducing the topological phase transition from DSM to a trivial insulator20 20, implying their common origin lying in the topological nature.To gain a more direct insight, we next present how the 2D transport changes through the chemical-doping-induced topological phase transition as summarized in Fig. r20 Fig. . The absr20 Fig. . We note3As2 has a small effective mass, a slight confinement (thickness t < 40 nm) can easily induce sub-band formation in the bulk state, leading to 2D bulk QH states14. Similar observations were later reported in molecular beam epitaxy grown Cd3As2 films (t ≤ 35 nm)21 and Zn-doped films (t ≤ 35 nm)22. However, we emphasize that the 2D transport observed here is qualitatively different from such a confinement-induced one in that it is accompanied with a 3D bulk state. To clarify this point, we compare in Fig. x1−Znx)3As2 samples with similar sheet carrier density but different film thicknesses (35 nm and 95 nm). For the thinner sample, as indicated by the absence of quantum oscillations in the longitudinal MR, the bulk state is confined into the 2D sub-bands. These sub-bands hold a high-mobility 2DEG, giving rise to QH states with Rxx at zero. The band properties such as effective mass m* and Fermi velocity vF in the 2D sub-bands are almost identical to those of the original 3D bulk22. For the thicker film, the bulk Fermi surface remains 3D, meaning that the bulk state is still in a gapless DSM phase. While the surface QH states appear at higher fields, the bulk quantum oscillations remain on the background . For the x = 0.15 sample in Fig. n2D,QHE = 1.1 × 1012 cm−2 matches up with the total sheet carrier density of the film obtained from Ryx at low fields . This indicates that all sheet carriers including the bulk ones are involved in the surface QH states, even though the bulk state is not completely depleted under the applied field range. The similar tendency has been commonly observed for the QH states in bulk nano-plate samples12, though this fact has not been well appreciated so far . Therefore, it seems not appropriate to simply apply the original Weyl orbit formula characterized by the thickness-dependent phase term5, to the quantized transport. The involvement of bulk carriers also indicates that the bulk and surface conductions are neither independent nor parallel any more in the QH states. This characteristic feature may reflect the unique nature of the magnetic orbits in DSM where the bulk and surface states are woven together through their connectivity at the Weyl points, as discussed later.The sheet carrier-density involved in a QH state can be calculated through the relation EF to further demonstrate the bulk contribution to the surface transport. Figure VG scans of the MRs for a x = 0.19 sample at each field step. The x = 0.19 sample is still in the DSM phase, showing surface transport as indicated by the appearance of QH effect in Fig. VG and B mappings of oscillation peaks and valleys of Rxx are presented by taking the second derivative of the transverse and longitudinal MRs. With increasing the negative bias, a clear crossover from the bulk-dominant conduction into the surface QH effect appears around VG = −5 V. The VG scan of carrier density reveals the successive change in the filling factors ν of the surface QH states. In particular, a fourfold degeneracy is observed, for example in the VG scan at 6 T. If the magnetic orbits are completed only within the surface states, the degeneracy of the QH states should be 2. Thus, the observed degeneracy of 4 suggests that the actual orbits are formed by involving the bulk states, where the additional degeneracy can be explained by two kinds of bulk states forming different orbits.We next present the evolution of the surface QH states with tuning 3As2 bulk nano-plates (t ∼ 80 nm) especially around the field range before the bulk state reaches the quantum limit7. It has been suggested that two of the confinement-induced sub-bands derived from the bulk chiral mode (N = 0 LL) give rise to two pairs of Weyl orbits to explain the degeneracy. Our successive VG and B scans in Fig. N = 0 and N = 1 levels remain occupied below EF. Considering the non-chiral nature of the N = 1 level, the surface magnetic orbits mediated by this LL should be conventional ones connecting the double Fermi-arcs on each surface, not the Weyl orbit expected for the orbits via the bulk chiral mode. As the bulk N = 1 level is depleted by increasing VG or B, only the orbits via N = 0 level remain, reducing the total degeneracy to 2.The fourfold degeneracy of QH states has been also observed in CdN = 0 level is the Weyl orbit or the conventional surface orbit. In Fig. VG or B. The same degeneracy lifting is confirmed in the high-field regime for our film samples 3As2 films, we have demonstrated the emergence of surface QH states and their characteristic QH degeneracy dependence on the bulk LL occupation. To independently control each surface by accumulation/depletion or designing heterointerfaces with ferromagnets or superconductors is an important topic for further engineering the surface transport26. The interface control can also be useful to probe possible interplays between the Fermi-arcs and the bulk topological transport in DSM such as non-local charge pumping by chiral anomaly28, spin Hall effect29, node-separation-gradient-induced pseudo-magnetic field30, and exotic superconductivity32 through their connectivity in the momentum space. In this respect, our demonstration of the surface QH states controlled in thin films provides an essential platform for investigating the novel quantized transport phenomenon.Through the control of dimensionality, x1−Znx)3As2 (112) films were grown on SrTiO3 (100) single-crystal substrates by the combination of pulsed laser deposition technique and subsequent thermal annealing33. The film thickness is designed to be around 85–100 nm to maintain the three-dimensionality of the bulk state. To strictly define the current path in magnetoresistance measurements, the films were deposited through a stencil metal mask and patterned into a Hall bar shape with channel width of 60 μm. After the deposition, they were capped by MgO (5 nm)/Si3N4 (200 nm) to prevent their evaporation and oxidization in the subsequent thermal annealing at 600 °C in air. For the electrostatic gating measurements, a top-gate configuration was fabricated by thinning the topmost Si3N4 capping layer down to 10 nm by ion milling, and then depositing 40 nm thick Al2O3 as gate dielectric by atomic layer deposition, followed by the deposition of 50 nm thick Au as gate electrode . Hall measurements with electrostatic gating were conducted using a lock-in technique, where the excitation current was kept constant at 0.5 μA with a frequency of 13 Hz. The contour mappings in Fig. The magnetoresistance measurements up to 55 T were performed using a non-destructive pulsed magnet at the International MegaGauss Science Laboratory at the Institute for Solid State Physics of the University of Tokyo. Longitudinal resistance Supplementary Information


Our results show that Cdc13 protects the 3′ overhang at least 5 nucleotides (nt) beyond its binding site, when bound directly adjacent to the ds-ss junction. Rap1 protects 1–2 nt of the 3′ overhang when bound to dsDNA adjacent to the ds-ss junction. Remarkably, when Rap1 is bound across the ds-ss junction, the protection of the 3′ overhang is extended to 6 nt. This shows that binding by either Cdc13 or Rap1 can protect telomeric overhangs from 3′ exonucleolytic degradation, and suggests a new important role for Rap1 in protecting short overhangs under circumstances when Cdc13 cannot bind the telomere.Telomeres, the DNA-protein structures capping the ends of linear chromosomes, are important for regulating replicative senescence and maintaining genome stability. Telomeres consist of G-rich repetitive sequences that end in a G-rich single-stranded (ss) 3′ overhang, which is vital for telomere function. It is largely unknown how the 3′ overhang is protected against exonucleases. In budding yeast, double-stranded (ds) telomeric DNA is bound by Rap1, while ssDNA is bound by Cdc13. Here, we developed an  Highly specific telomere binding proteins interact with both the dsDNA and ssDNA in order to protect them from harmful DNA damage responses6. Due to the limitations of the DNA replication machinery, telomeres shorten with each replication cycle7. Most eukaryotes prevent telomere shortening, which otherwise leads to cellular senescence8, through extension of the 3′ overhang by telomerase reverse transcriptase11. In humans, telomerase maintains telomeres in germ line and stem cells, while most somatic cells do not express telomerase12. Re-activation of telomere lengthening, as well as genomic instability at unprotected telomeres, contributes to the development of cancer13.Telomeres are specialized nucleoprotein structures localized at the ends of linear chromosomes that protect and stabilize the genome. Telomeric DNA consists of repetitive G-rich sequences that extend in the 5′ to 3′ direction as double-stranded (ds) DNA and beyond into a single-stranded (ss) 3′ overhang14. Furthermore, in human cell lines without telomerase activity, telomere 3′ overhang length is proportional to the rate of telomere shortening per cell division15. Hence, the regulation of telomere 3′ overhang length might have implications for cell senescence and aging. Telomeric 3′ overhangs are also required for the formation of higher-order DNA structures that promote telomere protection, such as G-quadruplexes and telomere loops (t-loops)17. In higher eukaryotes, t-loop formation involves the fold back and invasion of the 3′ overhang into the telomeric double-stranded region of the same chromosomal end, providing protection by effectively sequestering the 3′ overhang18. The length of telomeric 3′ overhangs varies between species. The budding yeast Saccharomyces cerevisiae has short overhangs of 12–14 nucleotides (nt) for most of the cell cycle, with longer (>30 nt) overhangs forming during S-phase19. The length of human telomeric 3′ overhangs ranges from 12 up to several hundred nt22 and at least 6 nt was shown to be required for the formation of a t-loop structure in vitro23.The telomeric 3′ overhang is important as the substrate for telomerase and as the binding site for protective proteins26. Previous studies have focused on identifying and understanding the regulation of exonucleases acting on the 5′ end, which are needed to create the 3′ overhang. However, the 3′ overhang is also at the risk of being degraded by 3′ exonucleases, and 3′ trimming has been suggested as a means to generate preferred 3′ end permutations in some species28. A large number of 3′ exonucleases are present in eukaryotic cells29, yet few studies have specifically addressed how they might act at the telomere 3′ overhang.In order to generate 3′ overhangs, telomeres are subject to 5′ end resection, C-strand fill in, and extension by telomerasein vitro assay to study the protection of Naumovozyma castellii telomeres from 3′ exonucleolytic degradation, using E. coli Exonuclease T/RNase T (ExoT) as a model exonuclease. ExoT belongs to the DEDD family of 3′ exonucleases29. Members of this family of 3′ exonucleases include the exonuclease activities of DNA polymerases, the WRN helicase which has a function in telomeric DNA processing31, and the major mammalian 3′ exonucleases TREX1 and TREX233. Intriguingly, WRN helicase activity is necessary for resolution of t-loops and may have a role in preventing 3′ overhang shortening in vivo31. Hence, the commercially available 3′ exonuclease ExoT is suitable for an in vitro model of telomere 3′ end protection.We have developed an N. castellii is a budding yeast of the Saccharomycetaceae family. Due to its regular short repetitive telomeric repeats (5′-TCTGGGTG-3′) it facilitates precise studies of telomere binding proteins34. N. castellii telomeric 3′ overhangs are highly variable in length, ranging from the minimal measurable size of 14 nt up to 200 nt in unsynchronized cells35. Hence, N. castellii telomeric repeats and 3′ overhangs resemble those of humans. N. castellii homologs of the budding yeast telomeric ssDNA binding protein Cdc1336, and telomeric dsDNA binding protein Rap137, have previously been characterized.S. cerevisiae6, and is part of the three membered complex CST, which in both humans and budding yeast functions in regulating telomere overhang length39. N. castellii Cdc13 binds telomeric ssDNA with high affinity through an 8 nt minimal binding site (MBS) 5′-GTGTCTGG-3′36.Cdc13 recruits telomerase to the telomere end in 41. S. cerevisiae Rap1 binds telomeric dsDNA directly42 and is an essential protein required for different telomere functions, including silencing43, telomere length regulation44, protection against telomere-telomere fusion45 and preventing excessive 5′ end resection46. N. castellii Rap1 binds to the 12 bp MBS 5′-GGGTGTCTGGGT-3′ on telomeric dsDNA37. Binding occurs via two Myb-subdomains, and the MBS is divided in two half-sites (5′-GGTGT-3′ and 5′-TGGGT-3′) separated by one base redundant for high affinity binding47. N. castellii Rap1 has been found to also bind across the ds-ss junction of certain 5′ end permutations, causing the Rap1 and Cdc13 binding sites to partly overlap on some ds-ss junctions48.Rap1 is conserved as a telomere associated protein across multiple species, including vertebrate animals, interacting with human telomeres via telomere binding protein TRF2in vitro assay to evaluate how Rap1 and Cdc13 protect telomeres from 5′ exonucleolytic degradation, showing that both proteins can protect the 5′ end even when bound at some distance from the ds-ss junction49. In the work presented here, we have evaluated the ability of Cdc13 and Rap1 proteins to protect the telomeric 3′ overhang from 3′ exonucleolytic degradation through the use of an in vitro assay that utilizes recombinant N. castellii Cdc13 and Rap1, synthetic telomere mimicking DNA substrates and E. coli Exonuclease T. The homogenous telomeric repeats of N. castellii enable us to study the protection by the proteins at their exact binding sites relative to the ds-ss junction and the 3′ end.Previously we have used an in vivo, many of which participate in important events regarding the replication, proof-reading and repair of DNA. For this assay, ExoT was selected as a commercially available enzyme that removes nucleotides in the 3′ to 5′ direction, generating blunt ends in DNA molecules with a 3′ ssDNA extension50. The budding yeast N. castellii has an 8 nt regular telomere repeat which allows us to study the positional effect of the telomere binding proteins around the ds-ss junction of the telomere. The oligonucleotides used in the 3′DEPA contain the N. castellii telomeric repeat sequence and a short non-telomeric guide sequence. The annealing of the oligonucleotides produces a substrate that has a double-stranded region and a protruding 3′ overhang, mimicking the N. castellii telomere  in a technique we have named the 3′ DNA End Protection Assay (3′DEPA). There are several enzymes with 3′ exonucleolytic activity 39. When Cdc13 binds the 3′ overhang it is expected to provide protection against 3′exonucleases by steric hindrance. However, the role of Cdc13 in protecting the telomeric overhang from 3′ exonucleases has not yet been characterized at the molecular level in vitro. We set out to do so by employing the 3′DEPA with a telomeric probe containing 13 nt of dsDNA and 13 nt of ssDNA N. castellii telomeric sequence, named D13S13 (where the nomenclature DxSy is used throughout this work to denote that there are x number of base pairs of telomeric dsDNA and y number of ssDNA nucleotides in the telomeric 3′ overhang). This substrate contains a Cdc13 minimal binding site (MBS) immediately adjacent to the ds-ss junction   Fig. . InteresBS) Fig. .in vitro assay addressing this aspect of telomere end processing in budding yeast. Since it is not clear which endogenous 3′ exonucleases might act at budding yeast telomeres in vivo, we used the commercially available E. coli ExoT as a model 3′ exonuclease. While this model may not take some aspects (such as protein-protein interactions and sequence bias) of a hypothetical telomere specific endogenous 3′ exonuclease into account, it does clearly show that both Cdc13 and Rap1 have the potential to protect the telomere 3′ end from exonucleolytic degradation. Furthermore, the methodology can be applied to study the ability of other telomeric proteins to protect the 3′ overhang, like the Rap1-interacting proteins Rif1 and Rif251.Few studies prior to this one have specifically addressed how telomere binding proteins prevent telomere overhang degradation by 3′ exonucleases, and to our knowledge this is the first in vivo; Cdc13 should be able to protect most conceivable 3′ overhangs if they are long enough to contain a Cdc13 binding site, and if the overhang is long enough to contain multiple binding sites, the most distally positioned Cdc13 is expected to prevent 3′ exonucleases from degrading the overhang. A similar study was previously performed for humans, where the telomere ssDNA binding protein POT1 was shown to prevent degradation by the Werner syndrome helicase WRN, that contains a 3′ exonuclease activity that can specifically degrade human telomere overhangs52. Hence, N. castellii Cdc13 may have a similar role in preventing telomeric 3′ end degradation as POT1 does in humans.Cdc13 is the core telomere ssDNA binding protein in budding yeast, making it an obvious candidate for blocking 3′ exonucleases at the telomere overhang. Our results show that indeed, binding by Cdc13 is enough to stop the action of a 3′ exonuclease. This allows us to speculate about the situation 48. Our results suggest that in both these situations Rap1 can provide protection of the 3′ overhang. Even though the overhangs protected by Rap1 are shorter than those protected by Cdc13, it may still have physiological relevance. It was recently shown that a 6 nt primer, similar to the overhang protected by Rap1 on the D13S13 substrate studied here, can be extended by N. castellii telomerase53. Even the short 1–2 nt overhang protected by Rap1 when bound only to dsDNA  in N. castellii telomerase-negative cells occur by a copying mechanism that is initiated at an interstitial telomeric sequence imbedded within the subtelomeric region59. Possibly, a t-loop structure could be involved in the mechanistics of the ALT process, thus allowing for the excision of t-circles that could act as templates for the telomere elongation.The human Rap1 protein (hRap1) has also been shown to bind ds-ss junctions in vitro exonuclease assay to assess the ability of N. castellii Rap1 and Cdc13 to protect the telomeric ss-overhang from 3′ exonucleolytic degradation and found that either one can protect the 3′ overhang. The redundancy of having two core telomere binding proteins with this function, suggests it is an important aspect of telomere protection and maintenance. Having telomeric 3′ overhangs of the right length is crucial for proper telomere function, and the mechanisms for generating and regulating telomeric 3′ overhangs has been extensively studied46, yet few studies have specifically addressed how 3′ exonucleases are prevented from degrading the overhang. Our results indicate that two of the core proteins involved in telomere protection and maintenance, Cdc13 and Rap1, may have complementary roles in preventing exonucleolytic degradation of the telomeric 3′ overhangs.In conclusion, we designed an Oligonucleotides were synthesised by Eurofins Genomics, either gel purified in house or by the manufacturer. Sequences of oligonucleotides used in this paper are listed below Table .Table 1ON. castellii Cdc13, full length Rap1 and Rap1 DBD variants was performed as previously described49, with the addition that proteins were concentrated and buffer exchanged on Amicon Ultra centrifugal filters (Millipore) cut-off 10 kDA (Rap1) and 30 kDa (Cdc13).Expression and purification of 48, except the G-rich strand was radioactively labelled, annealing was performed with a 1.5 times excess of the C-rich (unlabelled) probe and all binding reactions contained 10 fmol of probe together with purified recombinant N. castellii Cdc13 or Rap1.EMSA was performed to assess protein binding and annealing of the substrates. EMSA was performed as previously described48. The concentrations used of Cdc13 and Rap1 were determined by EMSA to give ~60–100% shifted D13S13 or D15S11 probe , while producing negligible amounts of higher than 1:1 stoichiometry complexes. Following binding, a 15 μl volume was removed from the reaction batch to be used as a “no enzyme added” control. Exonuclease T  was added to the remaining reaction batch , which was then incubated at 25 °C. 15 μl volumes were removed from the reaction batch at the time points indicated in figure legends, mixed with 9.5 μl stop buffer  and incubated at 65 °C for at least 20 min. DNA products were then precipitated and separated by denaturing PAGE as previously described49, with the exception that a 19 nt radioactively labelled oligonucleotide was used as loading control.First, Cdc13 or Rap1 was allowed to bind the overhang substrate. BSA (diluted in the storage buffer of the respective proteins) was used as a non-DNA binding control in an identical reaction. The binding was performed under the same conditions used for EMSAs, except the reaction was scaled up to contain 97.5 μl, corresponding to 6.5 EMSA reaction volumesDried gels were exposed to phosphorimager screens and scanned using Typhoon FLA9500 imager (GE Life Sciences). Quantification was performed in the ImageQuant software (GE Life Sciences) from scans at 200 μm resolution (saved as GEL files). Lanes were manually selected, background removed using “rolling ball” with a radius of 200, bands automatically detected , followed by manual inspection and correction if needed. For 3′DEPAs the data was compiled in a table containing the volume of each band in every lane, which was exported to Excel (Microsoft) where calculations and graphs were made. The product remaining at each time point was calculated by dividing the sum of band volumes corresponding to certain overhang lengths (e.g. the upper 8 bands for a ≥6 nt overhang on D13S13) with the sum of all the product band volumes. The value of the “no enzyme added” control was set to 100%. For EMSAs, the band% of the major shifts and the unbound probe were determined in ImageQuant, and % of bound probe calculated as total (100%) minus band% of unbound probe.Lane profiles were generated in ImageQuant from GEL images scanned at 50 μm resolution by manually selecting lanes, which were exported as numerical values to Excel. Values were normalized to the loading control band volume of each lane, and values corresponding to the bands from the full length substrate through to the blunt ended product were plotted.Displayed gel images were converted from raw data GEL to TIFF files in ImageJ. In Adobe Photoshop the contrast was enhanced using the levels setting and the images were cropped before import to Adobe Illustrator where figures were assembled.Supplementary information