Biology subjects

Bartram, J.

Publications and source records attributed to Bartram, J..

16 recordsLinked to original sources

bioRxiv2026

Recurrent leukaemia stem cell states define the antigenic landscape of paediatric Acute Myeloid Leukaemia

Paediatric acute myeloid leukaemia (pAML) remains a leading cause of childhood cancer mortality, with disease heterogeneity representing a major obstacle to effective immunotherapy. To enable rational immunotherapy design accounting for such heterogeneity, we combined single-cell transcriptomics and surface proteomics (CITE-seq). Using an integrated framework for malignant cell identification and unbiased gene program identification, we resolved three conserved leukaemia stem cell (LSC) subtypes, primitive (P-LSC), monocytic (M-LSC), and erythroid-like (E-LSC), that frequently coexist within individual patients in varying proportions and represent distinct stem-cell states with characteristic immunophenotypes and transcriptional programmes. Quantitative surface antigen profiling revealed that no single target achieved full LSC coverage. CLEC12A, an antigen currently being evaluated clinically, was expressed across P-LSC and M-LSC but was largely absent from E-LSC. In contrast, CD84 provided complementary coverage in the P and M groups and served as the primary target in the E-LSC population. The combination of CLEC12A and CD84 provided broad coverage across all three LSC subtypes, while sparing healthy haematopoietic stem cells. Validation across independent single-cell and bulk cohorts confirmed the robustness of this combination. These findings establish a paediatric-specific framework for antigen prioritisation in AML, identify CLEC12A/CD84 as a robust dual-targeting strategy across heterogeneous LSC states, and provide a blueprint for the rational design of next-generation CAR T-cell combinations.

cancer biology↗

bioRxiv2025

Single cell transcriptional evolution of myeloid leukaemia of Down syndrome

Children with Down syndrome have a 150-fold increased risk of developing myeloid leukaemia (ML-DS). Unusually for a childhood leukaemia, ML-DS arises from a preleukaemic state, termed transient abnormal myelopoiesis (TAM), via a conserved sequence of mutations. Here, we examined the relationship between the genetic and transcriptional evolution of ML-DS from natural variation; a rich collection of primary patient samples and fetal tissues with a range of constitutional karyotypes. We distilled transcriptional consequences of each genetic step in ML-DS evolution, utilising single cell mRNA sequencing, complemented by phylogenetic analyses in progressive disease. We found that transcriptional changes induced by the TAM-defining GATA1 mutations are retained in, and account for most of the ML-DS transcriptome. The GATA1 transcriptome pervaded all stages of ML-DS, including progressive disease that had undergone genetic evolution. Our approach delineates the transcriptional evolution of ML-DS and provides an analytical blueprint for distilling consequences of mutations within their pathophysiological context.

cancer biology↗

bioRxiv2025

Bi-specific CAR-iNKT cell immunotherapy for high-risk KMT2A-rearranged leukemia outperforms CAR-T in an NKG2D-dependent manner and eradicates leptomeningeal disease

Current therapies, including autologous CAR-T immunotherapy, fail to cure half of infants with KMT2A-rearranged acute lymphoblastic leukemia (KMT2Ar-ALL). Here we deploy allogeneic iNKT cells, innately more powerful effectors than T cells, and equip them with CD19- and/or CD133-targeting CARs. Compared to mono-specific counterparts and bi-specific CAR-T, CD19-CD133 bi-specific CAR-iNKT have more potent anti-leukemia activity, they effectively target CAR antigen-low leukemia, eradicate medullary and leptomeningeal leukemia and induce sustained remissions without discernible hematologic toxicity. Mechanistically, dynamic CAR- and CAR antigen-dependent upregulation of the activating innate receptor NKG2D and its engagement by corresponding ligands on KMT2Ar-ALL cells lead to more potent anti-leukemia effect of CAR-iNKT over CAR-T cells, including against CAR antigen-negative leukemia. Thus, by engaging with two different types of leukemia-associated targets, CAR-iNKT provide a powerful platform for the treatment of KMT2Ar-ALL. This approach can be readily adapted for other high-risk malignancies, including those with otherwise difficult to target leptomeningeal involvement.

cancer biology↗

bioRxiv2024

Gum Arabic (Acacia senegal) enhances reproduction and modulates the microbiota-gut-brain axis of zebrafish in a sex-specific and dosage-dependent manner

Dietary fibres (DFs) constitute a wide range of heterogeneous compounds that resist digestion and have beneficial effects on general health. Gum Arabic (GA) is a tree exudate consisting of 90% arabinogalactan, a polymer of arabinose and galactose sugars with prebiotic properties. As a dietary fibre, GA improves renal function, metabolism, and immune response in humans and animals. However, the underlying mechanisms leading to these health benefits are poorly understood. We supplemented female and male zebrafish (Danio rerio) with two concentrations of GA (6% and 60%) for two weeks. We assessed the effects of GA supplementation on the gut microbiome composition, intestinal and brain metabolic profiles, reproductive fitness, and brain gene expression. We found that GA supplementation resulted in changes to the gut microbiome with a relative increase in Fusobacteria and a relative decrease in Proteobacteria where the beneficial genus Cetobacterium was significantly more abundant after supplementation. GA supplementation increased acetate levels, particularly in the brain, causing a decreased expression of cart1 in the brain of female zebrafish. While GA supplementation increased overall activity in male and female fish, reproductive fitness was negatively affected by GA supplementation in females. Our results suggest that while GA supplementation may have positive effects on metabolic rate and overall activity, it may come at a trade-off with reproductive fitness. Significance StatementDietary fibres, found in plant-based food sources, can improve health. They include natural gums like gum Arabic, a highly sought-after food additive used as a homogeniser. Despite our better understanding of nutrition, a fibre gap is still prevalent in the Western world with efforts being made to incorporate new sources to close this gap and boost well-being. Here, we showed that when gum Arabic was supplemented into the zebrafish diet, it had a beneficial modulatory effect on the microbiota-gut-brain axis and reproductive fitness. Our findings support the benefits of dietary fibres but also link their impact to sexual dimorphism and dosage. This has implications for developing nutrition guidelines for both animals and humans.

systems biology↗

bioRxiv2024

RT-Sort: an action potential propagation-based algorithm for real time spike detection and sorting with millisecond latencies

With the use of high density multi electrode recording devices, electrophysiological signals resulting from action potentials of individual neurons can now be reliably detected on multiple adjacent recording electrodes both in vivo and in vitro. Spike sorting assigns these signals to putative neural sources. However, until now, spike sorting can only be performed after completion of the recording, preventing true real time usage of spike sorting algorithms. Utilizing the unique propagation patterns of action potentials along axons detected as high fidelity sequential activations on adjacent electrodes, together with a convolutional neural network based spike detection algorithm, we introduce RT-Sort (Real Time Sorting), a spike sorting algorithm that enables the sorted detection of action potentials within 7.5ms{+/-}1.5ms (mean{+/-}STD) after the waveform trough while the recording remains ongoing. RT-Sorts true real-time spike sorting capabilities enable closed loop experiments with latencies comparable to synaptic delay times. We show RT-Sorts performance on both Multi-Electrode Arrays as well as Neuropixels probes to exemplify RT-Sorts functionality on different types of recording hardware and electrode configurations.

neuroscience↗

bioRxiv2024

Ensemble learning and ground-truth validation of synaptic connectivity inferred from spike trains

Probing the architecture of neuronal circuits and the principles that underlie their functional organization remains an important challenge of modern neurosciences. This holds true, in particular, for the inference of neuronal connectivity from large-scale extracellular recordings. Despite the popularity of this approach and a number of elaborate methods to reconstruct networks, the degree to which synaptic connections can be reconstructed from spike-train recordings alone remains controversial. Here, we provide a framework to probe and compare connectivity inference algorithms, using a combination of synthetic and empirical ground-truth data sets, obtained from simulations and parallel single-cell patch-clamp and high-density microelectrode array (HD-MEA) recordings in vitro. We find that reconstruction performance critically depends on the regularity of the recorded spontaneous activity, i.e., their dynamical regime, the type of connectivity, and the amount of available spike train data. We find gross differences between different algorithms, and many algorithms have difficulties in detecting inhibitory connections. We therefore introduce an ensemble artificial neural network (eANN) to improve connectivity inference. We train the eANN on the validated outputs of six established inference algorithms, and show how it improves network reconstruction accuracy and robustness. Overall, the eANN was robust across different dynamical regimes, with shorter recording time, and ameliorated the identification of synaptic connections, in particular inhibitory ones. Results indicated that the eANN also improved the topological characterization of neuronal networks. The presented methodology contributes to advancing the performance of inference algorithms and facilitates our understanding of how neuronal activity relates to synaptic connectivity. Author summaryThis study introduces an ensemble artificial neural network (eANN) to infer neuronal connectivity from multi-unit spike time recordings. We compare the eANN to previous algorithms and validate it using simulations and HD-MEA/patch-clamp datasets. The latter is obtained from three single-cell patch-clamp recordings and high-density microelectrode array (HD-MEA) measurements, in parallel. Our results demonstrate that the eANN outperforms all other algorithms across different dynamical regimes and provides a more accurate description of the underlying topological organization of the studied networks. We also provide a SHAP analysis of the trained eANN to understand which input features of the eANN contribute most to this superior performance. The eANN is a promising approach to improve connectivity inference from spike-train data.

neuroscience↗

bioRxiv2023

Protosequences in human cortical organoids model intrinsic states in the developing cortex

Neuronal firing sequences are thought to be the basic building blocks of neural coding and information broadcasting within the brain. However, when sequences emerge during neurodevelopment remains unknown. We demonstrate that structured firing sequences are present in spontaneous activity of human and murine brain organoids and ex vivo neonatal brain slices from the murine somatosensory cortex. We observed a balance between temporally rigid and flexible firing patterns that are emergent phenomena in human and murine brain organoids and early postnatal murine somatosensory cortex, but not in primary dissociated cortical cultures. Our findings suggest that temporal sequences do not arise in an experience-dependent manner, but are rather constrained by an innate preconfigured architecture established during neurogenesis. These findings highlight the potential for brain organoids to further explore how exogenous inputs can be used to refine neuronal circuits and enable new studies into the genetic mechanisms that govern assembly of functional circuitry during early human brain development.

neuroscience↗

bioRxiv2023

A detailed input-output characterization of single neurons reveals the synaptic basis of spontaneous spiking in recurrent networks

Self-sustained recurrent activity in cortical networks is thought to be important for multiple crucial processes, including circuit development and homeostasis. Yet, the precise relationship between synaptic input patterns and the spiking output of individual neurons remains largely unresolved. Here, we developed, validated and applied a novel in vitro experimental platform and analytical procedures that provide - for individual neurons - simultaneous excitatory and inhibitory synaptic activity estimates during recurrent network activity. Our approach combines whole-network high-density microelectrode array (HD-MEA) recordings from rat neuronal cultures with patch clamping and enables a comprehensive mapping and characterization of active incoming connections to single postsynaptic neurons. We found that, during network states with excitation(E)-inhibition(I) balance, postsynaptic spiking often coincided with the maxima of fast fluctuations in the input E/I ratio. These spike-associated E/I ratio escalations were largely due to a rapid bidirectional change in synaptic inhibition that was modulated by the network-activity level. Our approach also uncovered the underlying circuit architecture and we show that individual neurons received a few key inhibitory connections - often from special hub neurons - that were instrumental in controlling postsynaptic spiking. Balanced network theory predicts dynamical regimes governed by small and rapid input fluctuation and featuring a fast neuronal responsiveness. Our findings - obtained in self-organized neuronal cultures - suggest that the emergence of these favorable regimes and associated network architectures is an inherent property of cortical networks in general.

neuroscience↗

bioRxiv2022

A CMOS-based highly scalable flexible neural electrode interface

Perception, thoughts, and actions are encoded by the coordinated activity of large neuronal populations spread over large areas. Using thin film electrocorticography (ECoG) arrays, this cortical activity has been used to decode speech and individual finger movements, enabling neuroprosthetics, and to localize epileptic foci. However, the connectorization of these multi-thousand channel thin-film arrays to external circuitry is challenging; current state-of-the-art methods are complex, bulky, and unscalable. We address this shortcoming by developing an electrode connector based on an ultra-conformable thin film electrode array that self-assembles onto hard silicon chip sensors, such as microelectrode arrays (MEAs) or camera sensors enabling large channel counts at high density. The interconnects are formed using microfabricated electrode pads suspended by thin support arms, termed flex2chip. Capillary-assisted assembly drives the pads to deform towards the chip surface, and van der Waals forces maintain this deformation, establishing mechanical and Ohmic contact onto individual pixels. We demonstrate a 2200-channel array with a channel density of 272 channels / mm2 connected to the MEA through the flex2chip interconnection method. Thin film electrode arrays connected through the flex2chip successfully measured extracellular action potentials ex vivo. Furthermore, in a transgenic mouse model for absence epilepsy, Scn8a+/-, we observed highly variable propagation trajectories at micrometer scales, even across the duration of a single spike- and-wave discharge (SWD).

bioengineering↗

bioRxiv2022

Decision-making strategies of a coral reef fish given changing visual signal reliability

Making informed decisions is fundamental to intelligent life and is crucial to survival. The problem many organisms face, is how to make optimal decisions in the face of incomplete, unreliable, or conflicting information. In many aquatic environments, fish use visual information to guide key behaviours, but the environment itself can alter or mask the very signals they rely on. Here, we asked how a highly visual species, Rhinecanthus aculeatus, responds to a learned discrimination task as signal reliability decreases, and whether probabilistic information gained during previous experience can be incorporated into their decision strategy. Fish were first trained to select a target (dark grey circle) from three distractors (light grey circles). In the first experiment, the target was more likely to appear in one of four possible stimulus positions. In the second experiment, the target appeared in all positions equally. In a series of trials, the difference in brightness between the target and distractors was reduced until all four stimuli were identical. We found that target selection accuracy decreased with decreasing target and distractor disparity. However, in Experiment 1 where the target was more likely to be in one position, fish increasingly selected stimuli in the biased position as target selection accuracy decreased, but not in Experiment 2. These results demonstrate 1) that fish learned more than a simple select/avoid rule based on stimulus brightness, but also integrated information (stimulus position) that could be considered ancillary to the primary task. 2) Fish can learn probability distributions and apply this knowledge as uncertainty increases, ultimately increasing the overall frequency of correct choices. Our results reveal that probabilistic decision rules can be used by fish when visual information is unreliable, indicating a possible mechanism for decision making given the inherent noise in incoming sensory information.

animal behavior and cognition↗

bioRxiv2022

A multi-modal fitting approach to construct single-neuron models with patch clamp and high-density microelectrode arrays

In computational neuroscience, multicompartment models are among the most biophysically realistic representations of single neurons. Constructing such models usually involves the use of the patch-clamp technique to record somatic voltage signals under different experimental conditions. The experimental data are then used to fit the many parameters of the model. While patching of the soma is currently the gold-standard approach to build multicompartment models, several studies have also evidenced a richness of dynamics in dendritic and axonal sections. Recording from the soma alone makes it hard to observe and correctly parameterize the activity of non-somatic compartments. In order to provide a richer set of data as input to multicompartment models, we here investigate the combination of somatic patch-clamp recordings with recordings of high-density micro-electrode arrays (HD-MEAs). HD-MEAs enable the observation of extracellular potentials and neural activity of neuronal compartments at sub-cellular resolution. In this work, we introduce a novel framework to combine patch-clamp and HD-MEA data to construct multicompartment models. We first validate our method on a ground-truth model with known parameters and show that the use of features extracted from extracellular signals, in addition to intracellular ones, yields models enabling better fits than using intracellular features alone. We also demonstrate our procedure using experimental data by constructing cell models from in vitro cell cultures. The proposed multi-modal fitting procedure has the potential to augment the modeling efforts of the computational neuroscience community and to provide the field with neuronal models that are more realistic and can be better validated. Author SummaryMulticompartment models are one of the most biophysically detailed representations of single neurons. The vast majority of these models are built using experimental data from somatic recordings. However, neurons are much more than just their soma and one needs recordings from distal neurites to build an accurate model. In this article, we combine the patch-clamp technique with extracellular high-density microelectrode arrays (HD-MEAs) to compensate this shortcoming. In fact, HD-MEAs readouts allow one to record the neuronal signal in the entire axonal arbor. We show that the proposed multi-modal strategy is superior to the use of patch clamp alone using an existing model as ground-truth. Finally, we show an application of this strategy on experimental data from cultured neurons.

neuroscience↗

bioRxiv2022

Clinical application of tumour in normal contamination assessment from whole genome sequencing

The unexpected contamination of normal samples with tumour cells reduces variant detection sensitivity, compromising downstream analyses in canonical tumour-normal analyses. Leveraging whole-genome sequencing data available at Genomics England, we develop a tool for normal sample contamination assessment, which we validate in silico and against minimal residual disease testing. From a systematic review of 771 patients with haematological malignancies and sarcomas, we find contamination across a range of cancer clinical indications and DNA sources, with highest prevalence in saliva samples from acute myeloid leukaemia patients, and sorted CD3+ T-cells from myeloproliferative neoplasms. Further exploration reveals 108 hotspot mutations in genes associated with haematological cancers at risk of being subtracted by standard variant calling pipelines. Our work highlights the importance of contamination assessment for accurate somatic variants detection in research and clinical settings, especially with large-scale sequencing projects being utilised to deliver accurate data from which to make clinical decisions for patient care.

bioinformatics↗

bioRxiv2022

Inferring monosynaptic connections from paired dendritic spine Ca2+ imaging and large-scale recording of extracellular spiking

Techniques to identify monosynaptic connections between neurons have been vital for neuroscience research, facilitating important advancements concerning network topology, synaptic plasticity, and synaptic integration, among others. Here, we introduce a novel approach to identify and monitor monosynaptic connections using high-resolution dendritic spine Ca2+ imaging combined with simultaneous large-scale recording of extracellular electrical activity by means of high-density microelectrode arrays (HD-MEAs). We introduce an easily adoptable analysis pipeline that associates the imaged spine with its presynaptic unit and test it on in vitro recordings. The method is further validated and optimized by simulating synaptically-evoked spine Ca2+ transients based on measured spike trains in order to obtain simulated ground-truth connections. The proposed approach offers unique advantages as i) it can be used to identify monosynaptic connections with an accurate localization of the synapse within the dendritic tree, ii) it provides precise information of presynaptic spiking, and iii) postsynaptic spine Ca2+ signals and, finally, iv) the non-invasive nature of the proposed method allows for long-term measurements. The analysis toolkit together with the rich data sets that were acquired are made publicly available for further exploration by the research community.

neuroscience↗

bioRxiv2021

Single cell mRNA signals reveal a distinct developmental state of KMT2A-rearranged infant B-cell acute lymphoblastic leukemia

Infant B-cell acute lymphoblastic leukemia (B-ALL) has not followed the increasing trend towards cure seen in other childhood B-ALLs. The prognosis for infants with KMT2A gene fusions is especially poor, and the origins of this aggressive leukemia remain unknown. Here, we investigated the developmental state of KMT2A-rearranged infant B-ALL within hematopoietic hierarchies of human fetal bone marrow, using bulk mRNA meta-analysis of childhood leukemia and examination of single lymphoblast transcriptomes. KMT2A-rearranged infant B-ALL was uniquely dominated by an early lymphocyte precursor (ELP) state. Direct comparison of infant lymphoblasts with ELP cells distilled the core oncogenic transcriptome of cancer cells which harboured potentially targetable hybrid myeloid-lymphoid features. Overall our quantitative molecular analyses demonstrate a distinct developmental state of KMT2A-rearranged infant B-ALL.

genomics↗

bioRxiv2020

A novel human fetal liver-derived model reveals that MLL-AF4 drives a distinct fetal gene expression program in infant ALL

Although 90% of children with acute lymphoblastic leukemia (ALL) are now cured1, the prognosis of infant-ALL (diagnosis within the first year of life) remains dismal2. Infant-ALL is usually caused by a single genetic hit that arises in utero: rearrangement of the MLL/KMT2A gene (MLL-r). This is sufficient to give rise to a uniquely aggressive and treatment-refractory leukemia compared to older children with the same MLL-r3-5. The reasons for disparate outcomes in patients of different ages with identical driver mutations are unknown. This paper addresses the hypothesis that fetal-specific gene expression programs co-operate with MLL-AF4 to initiate and maintain infant-ALL. Using direct comparison of fetal and adult HSC and progenitor transcriptomes we identify fetal-specific gene expression programs in primary human cells. We show that MLL-AF4-driven infant-ALL, but not MLL-AF4 childhood-ALL, displays expression of fetal-specific genes. In a direct test of this observation, we find that CRISPR-Cas9 gene editing of primary human fetal liver cells to produce a t(4;11)/MLL-AF4 translocation replicates the clinical features of infant-ALL and drives infant-ALL-specific and fetal-specific gene expression programs. These data strongly support the hypothesis that fetal-specific gene expression programs co-operate with MLL-AF4 to initiate and maintain the distinct biology of infant-ALL.

cancer biology↗

bioRxiv2018

Motor cortex can modulate somatosensory processing via cortico-thalamo-cortical pathway

The somatosensory and motor systems are intricately linked, providing several routes for the sensorimotor interactions necessary for haptic processing. Here, we used electrical and optogenetic stimulation to study the circuits that enable primary motor cortex (M1) to exert top-down modulation of whisker-evoked responses, at the levels of brain stem, thalamus and somatosensory cortex (S1). We find that activation of M1 drives somatosensory responsive cells at all levels, and that this excitation is followed by a period of tactile suppression, which gradually increases in strength along the ascending somatosensory pathway. Using optogenetic stimulation in the layer-specific Cre driver lines, we find that activation of layer VI cortico-thalamic neurons is sufficient to drive spiking in higher order thalamus, and that this is reliably followed by excitation of S1, suggesting a cross-modal cortico-thalamo-cortical pathway. Cortico-thalamic excitation predicts the degree of subsequent tactile suppression, consistent with a strong role for thalamic circuits in the expression of inhibitory sensorimotor interactions. These results provide evidence of a role for M1 in dynamic modulation of S1, largely under cortico-thalamic control.

neuroscience↗