The ability to create three-dimensional (3D) models of brain tissue from patient-derived cells, would open new possibilities in studying the neuropathology of disorders such as epilepsy and schizophrenia. While organoid culture has provided impressive examples of patient-specific models, the generation of organised 3D structures remains a challenge. 3D bioprinting is a rapidly developing technology where living cells, encapsulated in suitable bioink matrices, are printed to form 3D structures. 3D bioprinting may provide the capability to organise neuronal populations in 3D, through layer-by-layer deposition, and thereby recapitulate the complexity of neural tissue. However, printing neuron cells raises particular challenges since the biomaterial environment must be of appropriate softness to allow for the neurite extension, properties which are anathema to building self-supporting 3D structures. Here, we review the topic of 3D bioprinting of neurons, including critical discussions of hardware and bio-ink formulation requirements. Neurological disorders, such as schizophrenia, bipolar disorder, Parkinson’s disease, Alzheimer’s disease and epilepsy, affect nearly one in six people on the planet . Often tNew technologies are now emerging which enable a new way to study the brain—using miniature, ‘brain-like’ systems of neurons grown in the lab . Using tIn an example of a ‘bottom-up’ approach, neural organoids can be generated from self-organising stem cell populations. The organoid strategy has received considerable attention; although limitations in terms of structural organisation, and reproducibility, are recognised. The complementary technology for ‘top-down’ biofabrication of 3D neural structures is a new frontier, which may be able to address some of the limitations of organoid strategies. However, the goal of 3D bioprinting neurons brings its own challenges, namely that of developing bio-ink formulations, which are simultaneously ‘printable’ and permissive to neural differentiation and neurite outgrowth.Here, we review the topic of 3D bioprinting of neurons, with a focus on epilepsy modelling as an example application. In particular, we aim to provide critical discussions of several important questions including: Why 3D? Why 3D bioprinting? What material properties are required to achieve 3D neural networks? What are the hardware and biomaterial constraints for 3D bioprinting, and how do these conflict with requirements for 3D neuronal culture? What is the state of the art for 3D bioprinting neurons?Epilepsy is the fourth most prevalent neurological condition worldwide and is a debilitating and poorly understood disorder. Epileptic disorders present as uncontrollable seizures which can be dangerous to the patient. Patients may have congenital epilepsy, or it may develop throughout life ,8. ThereTreatment can involve dietary changes, surgical intervention or antiepileptic drugs (AEDs). AEDs can be used as monotherapies or in combination with other treatments including other AEDs ,9,10. HeElectrode imaging in vivo only provides limited information about the brain activity of the patient; and current animal models of epilepsy have their shortcomings ,12. For Electrophysiological recording of brain activity in vivo is an invasive method that is limited by feasible electrode placement and health of experimental subjects . A non-iWhile harvesting a patient’s neurons to create a “disease-in-a-dish” model would be invasive and unethical, recent biotechnological developments have introduced the possibility of creating patient specific disease modelling using stem cells. In 2006 Yamanaka and Takahashi pioneered a method using retroviral vectors that could reprogram terminally differentiated mature murine fibroblasts to become pluripotent stem cells. Induced pluripotent stem cells (iPSCs) can differentiate into any cell type and can be generated by the introduction of four transcription factors, Oct4, Sox2, Nanog, and Lin28, into a target somatic cell. A major advantage of this technique is that the iPSCs are genetically identical to the donor cell . TherefoMa et al. 2004) demonstrated that stem cells grown in collagen gels with neural differentiation media, mimic the differentiation of central nervous system (CNS) development in vivo 04 demons. As a clOrganoids are simplified organs on a miniature scale A. Brain In a seminal 2013 paper, Lancaster et al. demonstrated many of these advantages including: Generation of cerebral organoids displaying discrete brain regions B; recapiFor example, Jo et al. (2016) developed organoids consisting of distinct layers of neuronal cells expressing characteristic markers of human midbrain. Electrophysiological recordings demonstrated the presence of functional neurons in approximately 20% of the population; and dopamine production in 3D midbrain-like organoids (MLOs) . NeuromeSome disease models derived from iPSCs have been developed for conditions which exhibit epileptiform activity , ,40 to daOrganoids possess many of the limitations inherent to other in vitro neural models, in that they do not mimic the 3D shape of the human embryonic brain. In vivo, the embryonic brain development is guided by the embryonic body axes which are lacking in vitro. Consistent spatial development has been achieved in organoids, however a large variability in size and tissue structure has limited the generation of disease pathogenesis models ,24,25,312D neuronal cultures have been the standard method for studying neuronal activity in vitro for several decades. The relative simplicity and versatility of the 2D set-up has allowed for many probing experiments to gain insight into the influence of substrate mechanics ,43 or naHowever, it is becoming increasingly recognised that 2D in vitro models are limited in their ability to model live tissue . The braAs described in a seminal review by Baker et al. (2012) two-dimensional, monolayer culture presents an environment that is quite different from the in vivo situation see A : (a) IIn addition to these considerations of how the microenvironment affects cell behaviour in 2D versus 3D, the dimensionality itself can have profound effects on how cells communicate. Neurons grown in 2D can only form synaptic connections with cells along one plane, meanwhile neurons grown in 3D culture can connect with surrounding neurons in all three dimensions see A. Lai etBourke et al. (2018) grew embryonic day 18 rat hippocampal cells in both 2D culture and as a 3D culture. The electrophysiological behaviour of neurons within both cultures was assessed via microelectrode array (MEA) at 35 days of culture B. Both 2+ compared to 3D cultures, where astrocytes have a tendency to overgrow [2D hydrogel cultures of astrocytes have also been demonstrated to yield lower populations of early astrocyte marker GFAPovergrow ,61. Cellovergrow ,62.To achieve 3D culture, cells are typically encapsulated in a hydrogel biomaterial which plays an early role as a rudimentary ECM ,55,56. TTo fabricate an anisotropically accurate model of the brain in vitro presents a formidable tissue engineering challenge. The brain is arguably the most highly complex organ in higher vertebrates, and is also extremely soft, with an elastic modulus of less than 1 kPa . As a stThe development and differentiation of precursor neural cells in vitro is a recapitulation of the development on the brain in utero. Thus, in vitro constructs aim to mimic the foetal brain microenvironment. Sood et al. (2016) characterized foetal and adult porcine brain ECM and found that foetal ECM was comprised of 417.8 µg/mg collagen and sGAG 406.6 µg/mg, whilst adult brain ECM had a pointedly higher sGAG to collagen ratio, with less collagen at 200.9 µg/mg, and sGAG concentration of 397 µg/mg . Sood etThis native composition suggests hydrogels as suitable materials for formulating in vitro neural culture, in particular collagen type I and hyaluronic acid.Microfluidic devices, also called ‘organ-chips’ or ‘lab-on-a-chip’, consist of several microchannels with volumes of microliters or picolitres, and can be fabricated with interconnected compartments containing 2D or 3D neural culture environments. Designed compartments allow individual cell analysis of cell secretions, protein, and gene expression. Channels can enable fluid flow that can introduce gradients and model the dynamic in vivo environment ,73. Micr® to culture human iPSC derived neural progenitor cells (NPCs), glial cells, and neurons embedded in Matrigel. OrganoPlate® consists of 96 tissue ‘chips’, which have four wells that feed via capillary action into channels: A gel inlet well, medium inlet well, medium outlet well, and an observation window. Functional neurons formed with network formation was assessed by Ca2+ imaging. The neurotoxins 2,5 hexanedione, endosufan and methylmercury were added to media in varying concentrations, and their effects on cell viability were assessed, however the effect on neurite outgrowth was only reported for methylmercury [Wevers et al. (2016) used the commercially available OrganoPlatelmercury . The 96 Microfluidic devices have also been used for ex vivo study. Hippocampal slice cultures spontaneously develop seizures within a week after dissection and are used as in vitro models of post-traumatic epilepsy . Liu et Here, we survey research into developing hydrogels for 3D culture of neurons. Hydrogels consist of hydrophilic polymer chains that crosslink to form a network. Hydrogels are an attractive material for culturing cells in 3D due to their biocompatibility, high water content, and tunable physical and chemical properties. Mechanical stiffness and pore size are tunable in many hydrogels to match hydrogel properties with the in vivo cellular environment ,83. In tTypically in the cell encapsulation process, cells are mixed with hydrogel precursors, which are then crosslinked via chemical means or changes in pH or temperature, with cells being encapsulated during the gelation process . NeuronaCollagen type I, hereafter referred to as collagen, is extracted from animal sources and stabilized in liquid form in acetic acid, it can then be crosslinked through a pH change or with chemical crosslinking agents such as genipin ,92. The As described above, collagen I is a native component of brain ECM in vivo, and provides binding sites encouraging neurite outgrowth such as RGD and target sequences of matrix metalloproteinase (MMP) . The proHyaluronic acid (HA) is a hydrophilic nonimmunogenic long chain polysaccharide found in the ECM of connective tissue; it is also integral to the structure of the CNS ,97. The HA concentration can be altered to create bioinks with tunable mechanical characteristics without changing the pore size of the scaffold ,101. In One of the most popular techniques to covalently crosslink hyaluronic acid is to functionalise the polymer chains with methacrylate groups, which can be photocrosslinked through a photo-induced free-radical polymerisation reaction. Higher degrees of functionalisation result in stiffer gels and slower degradation via hyaluronidase . The durSofter HAMA 3D cultures drive NPCs towards a neural phenotype, whereas HAMA 3D cultures with stiffer mechanical properties akin to an adult brain favoured NPC differentiation into astrocytes ,103. FunAnother strategy to achieve covalent crosslinking is thiolated HA (HA-SH), which has been used to synthesise gels with storage modulus comparable to native brain tissue at 188 ± 42 Pa . Howeverw/v, and so achieves compressive moduli on the order of 1000 Pa at its softest [Gelatin is hydrolyzed collagen and therefore shares many of the same ECM peptide motifs as collagen such as RGD and MMP sequences promoting cell adhesion. Gelatin methacryloyl (GelMA) is a hydrogel which covalently crosslinks via photoinitiated radical polymerization. GelMA is relatively tunable; both pore size and compressive modulus can be modulated by varying the degree of methacryloyl substitution . However softest . PC12 ne softest ,108. Neu softest   functionalized with methacrylate groups can reach compressive moduli in the range of 10–400 Pa, comparable to brain tissue, depending on concentration ,111. 0.5Algae derived alginates can be ionically crosslinked via the addition of a divalent cation such as calcium to form a hydrogel network. Palazzo et al. (2015) developed alginate hydrogels with storage moduli ranging from 10–4000 Pa, depending upon the alginate concentration and the concentration of the calcium crosslinker added. The lowest concentration of alginate, 0.1% had a storage modulus of 10 Pa with 83% of encapsulated rat primary cortical neurons viable after three days . CalciumMatrigel is a viscous protein-rich secretion from Engelbreth-Holm-Swarm (EHS) mouse sarcoma cells that contains many ECM components . Irons eFibrin hydrogels are produced from fibrinogen, which is derived from plasma containing Factor XIII, and crosslinks via the enzymatic activity of thrombin. Increased fibrinogen concentration results in gels of higher stiffness and decreased pore size, fibrinogen concentration 4 mg/mL, 6 mg/mL, 8 mg/mL and 10 mg/mL resulted in pore sizes of 0.71 µm, 0.65 µm, 0.53 µm and 0.49 µm; and elastic moduli of 400 Pa, 700 Pa, 1000 Pa and 1200 Pa . Bento eSynthetic poly(ethylene glycol) (PEG) hydrogels have been used to develop multicomponent neural constructs that mimic brain tissue . Thiol-e3D hydrogel cultures have been developed as brain disease models, using patient derived iPSCs or via manipulation of culture conditions. Although bulk gel 3D cultures lack the microarchitecture of native tissue, valuable insights have been gained via bulk gel disease models. High molecular weight HAMA has been utilized to develop 3D models of glial scar formation, with an elastic modulus similar to that of native brain tissue of 200 Pa . Neural To explore neural migration in Rhett syndrome, a symptom of which is epileptic seizures, patient derived iPSCs were compared to nonaffected parental control derived iPSCs. Rhett syndrome is attributed to mutations in the gene encoding methyl-CpG-binding protein-2 (MeCP2) . PatientApproximately 5% of people hospitalized for traumatic brain injury (TBI) develop post-traumatic epilepsy (PTE) . Al RifaIn a model for the pathogenesis of Alzheimer’s disease, 3D collagen matrix with embedded β-amyloid aggregates was seeded with PC12 cells, no toxicity was observed when cells were separated from β-amyloid aggregates . Only upChoi et al. (2014) used retroviral vectors to engineer NPCs that overexpressed familial Alzheimer’s disease mutations to produce high levels of β-amyloid peptides. Engineered NPCs were embedded in 3D Matrigel, and high levels of β-amyloid and hyper-phosphorylated tau accumulated in six weeks, compared to 2D cultures where β-amyloid peptides diffused into culture media. Application of β and γ secretase inhibitors significantly lowered β-amyloid levels and also tau, demonstrating that high levels of β-amyloid peptide contribute directly to tau pathology . This liLayered neural models of neurons encapsulated in bulk gels have provided insights on neural migration and neurite extension. Although 3D constructs can be created by simply pouring a bulk gel into a cast with high fidelity to the desired form, referred to as cast moulding , cell plLayered 3D neural models are formed by crosslinking one layer of a bulk gel prior to deposition of the next layer. These structures are typically created through manual pipetting, and so represent a link between the bulk 3D gels already discussed, and the more robotically controlled bioprinters to follow. Layered models have demonstrated that cell seeding onto 3D gels is inferior to cell encapsulation in 3D gels in recapitulating in vivo neural morphology . LayeredDurotactic gradients, gradients of increasing stiffness, were demonstrated to promote neurite extension compared to gels of uniform mechanical properties . Similar3D scaffolds serve as a rudimentary structure to guide cell growth via topographical cues; cells are seeded onto pre-formed scaffolds. A myriad of techniques has been developed for neural scaffolds with applications ranging from disease modelling to therapeutic implants. Herein, we describe neural scaffolds with a focus on guided neurite growth, mechanical and electrophysiological properties. Colloidal scaffolds consist of layers of microbeads; silk scaffolds have served as moulds for bulk hydrogels; methods to create macroporous scaffolds have been developed and electrospinning has been used to guide neurite extension.Colloidal scaffolds consist of layered microbead bead arrays, beads of silica , glass , and chiThe silk from silkworm cocoons can form a sponge like material with pore size ~500 µm and is stiff enough to be self-supporting. Tang-Schomer et al. (2014) developed 3D silk scaffolds with compartmentalized layered architectures as a cerebral model: Axon only ‘white matter’ in the centre and neurons in the outer layers as ‘grey matter’. . CollageMacropores are large pores acting as interconnected channels to facilitate mass transport and cell penetration throughout a 3D construct. Macropores can be fabricated using a pore creating agent, called a porogen ,131. ForCryogels are macroporous hydrogels where crosslinking occurs at subzero temperatures, ice crystals act as porogens, disrupting the crosslinking of the hydrogels, and once thawed leave macropores within the interconnecting micropores of the hydrogel . Due to A major limitation with such macroporous gels is that the low temperature or high salt concentrations, critical to the construction technique, are damaging to cells, so cells can only be added after the gel is fully formed, limiting cell encapsulation as a seeding method.Electrospinning is a technique used to create fine fibres with nano- or micrometer diameters. The technique involves using electric force to draw charged jets of polymer which are collected on a grounded collector; stationary collectors result in randomly oriented fibres while rapidly rotating collectors produce aligned fibres . ElectroStandard electrospinning techniques using a flat static collector or coaxial spinning mandrel result in a 2D matt of fibres ,140. OftMelt electrowriting (MEW) is a technique where polymer melts are directly written in a programable path, using a layer by layer technique MEW can be used to fabricate 3D electrospun structures . MEW is By combining two electrospun polymer inputs, HAMA and HA and/or zero and 3 mM RGD, in different ratios over the course of electrospinning, durotactic, and haptotactic gradients were achieved through the 3D scaffold . Cells f3D printing, also called additive manufacturing, creates 3D objects from a digital model by depositing or patterning material via a layer by layer approach. 3D printing, in this section, refers to applications where cells are seeded on a 3D scaffold after the printing process. A wide range of materials can be 3D printed, including hydrogels ,144,145.3D photopatterning refers to 3D printing techniques where light is used to draw a crosslinked pattern in a photocurable material or resin. Such techniques include stereolithography, which crosslinks in a point by point fashion until each layer is completed; digital light projection, which illuminates each layer, plane by plane , as wellPhotopatterning based single neuron capture in 3D GelMA microgel rings has been developed as a way to model autapse formation . A projeExtremely high-resolution 3D patterning can be achieved using multi-photon polymerization systems, where a beam of ultrafast infrared laser is focused to a point within a volume of photopolymerizable material. Since the polymerisation reaction requires multiple photons, the resin only hardens in a very small volume near the focal point . Multi-pExtrusion based 3D printing uses a robotically controlled syringe to extrude materials in a filamentous strand; which is written on the fabrication stage in a layer-by-layer approach. Extrusion printing requires biomaterials to have sufficient rheological properties, such as a high enough viscosity, to maintain the strand shape prior to crosslinking. Extrusion printing typically has lower resolution than inkjet or laser systems ,155.For neural applications, extrusion printing has been used to create 3D scaffolds from a range of materials including fibrin, where the aligned microstructure of 200 µm diameter fibrin fibers provided durotactic cues for Schwann cell alignment mimicking the bands of Bünger that Schwann cells form following nerve injury to guide axon regeneration. . In anotThe printability requirements for extrusion bioprinting typically favour materials which are far stiffer than the brain. However, some strategies have managed to side-step this issue, creating very soft 3D structures using extrusion printing. For example, Tan et al. (2017) used a cryogenic printing approach to create cuboid lattices with a compressive modulus of 490 Pa, matching well with stiffness of brain tissue . PVA andRecently, photopatterning techniques have been combined with other methods in convergent biofabrication strategies. For example, Sundararaghavan et al. (2010) combined photomask crosslinking with electrospun HAMA to introduce vascular like channels within the microporous 3D fibrous scaffolds; allowing mesenchymal stem cells (MSC) to penetrate throughout the 3D scaffold   model was created by dropping a weight from varying heights on 3D silk scaffolds with compartmentalized architectures filled with collagen and encapsulated primary cortical cells . At 1 miPhotopatterning 3D GelMA microgel rings has been developed as a way to model autapse formation; axonal circles developed in singly captured neurons . Autapsilamina molecularis has relatively few neurons, the thin lamina granularis externa is densely populated with small neurons, the lamina pyramidalis externa has medium pyramidal neurons, the lamina granularis interna consists of small stellate neurons, the lamina pyramidalis interna has large pyramidal neurons, and the lamina multiformis has polymorphic neurons [Interactions among multiple cell types determines the structure and function of all tissues. The brain is very complex with many regions and sub regions, for example it is estimated that there are 500–1000 different gray matter regions ,165. The neurons . ExquisiMicrofluidic casting utilizing polydimethylsiloxoane (PDMS) chips has been utilized to recreate complex layered 3D constructs, the dynamic environment offered by perfusion of microfluidic chips is a major advantage ,79,167. In 3D bioprinting cell laden hydrogels are typically deposited in liquid form in the desired shape prior to being crosslinked into a gel within minutes . Prior tThe goal of 3D bioprinting is to mimic the native microenvironment of in vivo tissue. A major hurdle to 3D biofabrication of soft structures, such as the brain, is the shaping of soft hydrogels into 3D structures with high spatial resolution that are an anisotropically accurate mimic of the brain microstructure. A gel with brain like mechanical properties ~500 Pa will sag under its own weight due to gravity resulting in a loss of shape due to the extremely soft characteristic of the gel. Soft 3D scaffolds have been developed with cryogels or cryogenic printing, however cells were seeded on scaffolds after the exposure to subzero temperatures ,158,175,A more straightforward approach is to use support structures, which support the soft structure during printing and during the maturation of cells to prevent deformation of the construct. This support structure needs to be biocompatible with the printed cells without affecting cell viability.Support structures can be classed into two broad categories: Permanent support structures or sacrificial scaffolds. A sacrificial support involves the printing of a structure, a support matrix, which can easily be removed after printing, often through a washing step . TherefoThe materials used for sacrificial supports vary greatly, however removal without harming cells is essential. Sacrificial scaffolds that dissolve quickly or require vigorous washing/flushing for removal are only appropriate for stiffer more cohesive gels, softer gels tend to disintegrate in the washing step . Water sHinton et al. (2015) developed a method of 3D printing soft materials called freeform reversible embedding of suspended hydrogels (FRESH), using a thermoreversible sacrificial support bath; unlike most 3D printing techniques this method is not restricted to layer-by-layer fabrication as demonstrated by the printing of a continuous 3D helix . The sup2 addition, before the sacrificial gelatin template was melted away at 37 °C. However, the softest hydrogel, 0.5% alginate, had a modulus of 2.5 kPa; and degraded within 3 h [Indirect bioprinting refers to the printing of complex support structures and infusing the support structure with cells encapsulated in hydrogel ,173. Nagthin 3 h . Cell vithin 3 h .Extrusion printing is commonly employed in bioprinting due to its economy, ease of use, and capability to print with high cell density with a wide range of materials ,170. ResParameters such as nozzle diameter, dispensing pressure, and material viscosity during bioprinting submit cells to the stresses of shear force, tension, and compression; if excessive these forces can result in deformation and cell damage. Shear stress can be minimized by using lower pressure/propelling force, less viscous materials, and larger diameter nozzles ,170. Cel2 or 5x Dulbecco’s Modified Eagle Medium (DMEM) and three layers were printed with neurons encapsulated in the bottom and top layer. Axon extensions spread throughout layers penetrating the acellular middle layer; neurons did not migrate between layers and networks formed within layers only  printed discrete layers of polysaccharide gellan gum-RGD (peptide modified biopolymer) and primary rat cortical neurons. The gellan gum-RGD was crosslinked via CaClers only A. Axon/d2 immersion. Printed constructs had a compression modulus of 4.73 kPa which decreased to 0.8 kPa at 10 days post printing where it stabilized, closer to the stiffness of native brain [Gu et al. (2016) extrusion printed human iPSCs that were differentiated post printing into neurons, astrocytes and oligodendrocytes, creating a neural mini-tissue 3D lattice refer to C. The bive brain . Neuriteve brain . Synaptove brain   used fused deposition extrusion printing to bioprint thermoresponsive biodegradable polyurethane dispersions of PCL diol & poly  diol (PU) with encapsulated murine NSCs; encapsulated 3D constructs were implanted into zebrafish that had sustained brain injuries  Figure D. RemarkAlthough extrusion is popular in 3D bioprinting it is only suitable for viscous gels, whereas inkjet deposits drop by drop making it more appropriate for soft gels  Figure A. In inkInkjet printing allows higher print resolution with precise cell placement due to the droplets deposited being in the picolitre range, the challenge with inkjet printing is building layers in the z direction due to the low viscosity of relevant materials, the only 3D inkjet printed neural model consisted of stacked layers of 2D patterned gels .−1 for 5 µs, which may account for the cell viability being only 72.4%, although it should be noted that no non-printed control viability was reported [2, cells were printed in an alginate bioink, which crosslinked the cells embedded within the hydrogel scaffold. Neurites were classified as axons if they were long with a consistent diameter or as dendrites if the diameter was thicker at cell soma with tapered extension. Neurite outgrowth was aligned with the PLA printed pattern in 71.9% ± 8.2% of axons and 49.3% ± 4.0% of dendrites after three days [In a pioneering work, Xu et al. (2005) utilized a commercial desktop printer equipped with thermal print heads to print embryonic motor neurons onto a 2D sheet of collagen, forming a neural ring after two days; and 40 µm dendritic extensions were observed in two cells after seven days . In a lareported   is a nozzle free method were cells are levitated by the reflection and interference of standing acoustic waves; crosslinking methods are required to be rapid to entrap levitated cells before cell settling occurs  Figure B. 3D fib3) was alternated with dispensed collagen layers and bioprinted cells, completing crosslinking and entrapping cells in their spatially defined position. For cell patterning at least one acellular collagen layer separated cell laden layers; distinct cell layers required separation by multiple acellular layers (Microvalve printing is a drop by drop or continuous filament method where droplet formation, with volumes in the order of 10 nanoliters, is controlled via adjustments to the applied pressure and valve opening duration A. Printir layers F. 3D mulr layers G,H; demor layers I,J; incrLike microvalve printing, microfluidic printing relies on pressure to control the flow rate; however, the bioink is crosslinked prior to deposition at a triton shaped junction where the crosslinking channel, buffer channel, and bioink channel meet B. CrosslUV laser writing B was usePiezoelectric inkjet bioprinting has given the highest resolution in printing neural cultures 25 ± 5 µm  with higThe most spatially defined 3D neural models have been fabricated via extrusion printing, typically as lattice structures ,184,200.Patient specific disease modelling of neural disorders is feasible with iPSCs directed differentiation into neural tissue. Creation of a neural disease model that accurately mimics brain tissue is a difficult task due to the highly complex tissue microarchitecture of the brain. Brain tissue is heterogeneous regarding cell populations and mechanical properties, and very soft. Brain organoids have been developed but results have had poor reproducibility, limiting their application in disease modelling. Organoids are limited in size and develop a necrotic core if grown too large due to lack of vascularization.3D neural models require biomaterials that mimic the mechanical properties of the brain and support the development of functional neural networks. Hydrogels mimic the poro-viscoelastic properties of brain tissue and have been demonstrated to support neural growth. Functionally active neural networks in 3D have been established in bulk gels of low concentration, and therefore soft, hydrogels of crosslinked collagen, hyaluronic acid and alginate. Bulk gels are simplistic due to the random cell distribution and cell settling effects, the small pore size also limits mass transport throughout the gel restricting the size of the model. 3D scaffolds introduce macropores to facilitate mass transport, 3D printed scaffolds have specifically designed pores or channels, in a highly reproducible automated manner. Cells are seeded onto scaffolds, and often have poor infiltration of the scaffold. Cells grow along the surfaces of the scaffold resulting in unnatural constraints affecting morphology and network disconnection between scaffold areas. 3D bioprinting of cells within the scaffold structure is a way of overcoming these limitations, while also allowing precise cell patterning. 3D bioprinting of neural models has been restricted by the constraints of developing a bioink that is soft enough to allow functional network formation and synaptogenesis, yet printable and capable of maintaining a 3D shape. These conflicting requirements present technical challenges, which may be overcome by using sacrificial support scaffolding, sacrificial baths or novel crosslinking methods. Most neural bioprinting studies have focused on cell survival post printing, developing bioinks and demonstrating neural differentiation in printed constructs. Demonstrating established functional networks in bioprinted 3D in a reproducible manner is the first milestone to be achieved. The notion of using bioprinting to specifically arrange cells to replicate heterogeneous cell distribution within the brain has only been explored minimally with prototype models of alternating or criss-cross cell patterns, and only with two different cell types. Patterning of multiple cell types including different glial populations with neural cells is necessary to mirror the varying cell populations within different brain regions. In the context of disease modelling, iPSCs could be used to grow patient specific organoids that spontaneously develop distinct brain regions. Cells from each brain region could be harvested and encapsulated in a suitable bioink for patterning via 3D bioprinting in a reproducible manner. An alternative method would be to incorporate differentiation cues within the bioink to develop spatially directed differentiation in a bioprinted organoid approach. Durotactic and haptotactic cues combined with growth factor gradients could further guide cell migration and neurite extension to achieve complexity in designed tissue microarchitecture.Vascularized neural models have not yet been developed. Incorporation of channels to allow mass transport of waste, nutrients, and oxygen will allow the survival of larger neural models. Dynamic flow as used in microfluidic devices will further recapitulate the in vivo microenviroment, in place of vasculature. For disease modelling replication of the blood brain barrier is important to determine whether prospective treatments will reach the target tissue at an effective concentration, if they are effective at all.3D bioprinting of neural models is still in the early stages of development and offers great potential for exquisite spatial bioink patterning to recapitulate the microarchitecture of brain tissue. Although 3D bioprinted neural disease models have not yet been developed, the potential advantages over animal models include species and patient specific disease modelling. First, neural models must be bioprinted with demonstrated functionally active neural networks; only then can bioprinted neural disease models be compared to animal disease models. The technology to create 3D models of brain tissue by bioprinting neurons is still in its infancy, but holds great promise as a new tool for studying the brain.


All calves had ad libitum access to a commercial calf starter and water. Analysis of the transcriptome profiles at 54 ± 2 days of age revealed that a total of 396 out of 19,968 genes were differentially expressed (DE) between groups . The directional expression of DE genes through Ingenuity Pathway Analysis showed that an enhanced nutrient supply alters adipose tissue physiology of pre-weaned calves. Several biological functions were increased (Z-score > +2), including Lipid Metabolism (Fatty Acid Metabolism), Cell Cycle , Cellular Assembly and Organization (Cytoskeleton Formation and Cytoplasm Development) and Molecular Transport (Transport of Carboxylic Acid). These changes were potentially orchestrated by the activation/inhibition of 17 upstream regulators genes. Our findings indicate that adipose tissue of calves under an enhanced nutrient supply is physiologically distinct from restricted calves due to an increased development/expansion rate and also a higher metabolic activity through increased fatty acid metabolism.Performance of dairy cows can be influenced by early life nutrient supply. Adipose tissue is diet sensitive and an important component in that process as it is involved in the regulation of energetic, reproductive and immunological functions. However, it is not clear how early life nutrition alters the molecular regulation of adipose tissue in calves and potentially adult individuals. This study aimed at determining how differences in pre-weaning nutrient supply alter gene expression profiles and physiology in omental adipose tissue. A total of 12 female Holstein calves were fed two levels of milk replacer supply: a restricted amount of 11.72 MJ of metabolizable energy (ME) intake per day (n = 6) or an enhanced amount of 1.26 MJ ME intake per kg of metabolic body weight (BW Early life is a critical period in which environmental factors such as nutrition can induce developmental plasticity towards altered biological functions in adulthood. Any event that occurs during this period can permanently influence the physiology of organisms: a process termed ‘programming’ or more recently ‘imprinting’. The conIt is important to consider that dairy calves are traditionally restricted in milk or milk-replacer supply to approximately 10% of bodyweight as milk, which represents less than half of their voluntary consumption ,8,9. ThiAdipose tissue is critical in animal development as it participates in several biological processes, including energy balance, endocrine, thermogenic, reproductive, and immune functions ,14,15. ITranscriptomic studies of key metabolic tissues offer a unique opportunity to understand how dietary manipulations can affect the physiology and development of farm animals by revealing a comprehensive view of the cellular and molecular regulation at a transcriptome level. We hypothesized that nutrient supply alters adipose tissue development and function in pre-weaning calves. Therefore, the objective of the present study was to determine how lifting the traditional nutrient supply in pre-weaning calves affects adipose tissue transcriptome expression and physiology.0.75 in three daily feedings. The supply of milk replacer (MR) was adjusted weekly according to changes in BW. Daily amounts of energy supply to each calf increased from 17.58 to 35.17 MJ per day. Calves in the restricted group were fed 11.72 MJ of ME per day in two daily feedings throughout the study, reflecting traditional industry standards. From day 18 onwards, calves were also offered a commercially available concentrate. All calves had access to water and were housed in individual hutches. Information on the composition of milk replacer and concentrate are provided in The experimental protocols describing the management and animal care have previously been reported . They weAt 54 days of age, calves were fed at 0700h, weighed and transported to the Cornell University abattoir . Calves were harvested approximately 4h after feeding by stunning with a captive bolt, followed directly by exsanguination. Immediately after exsanguination, a representative sample of omental fat was placed in foil bags and snapped frozen on liquid nitrogen. Samples were then transferred into a -80°C freezer until the gene expression profiling analyses were conducted.Total RNA was prepared by homogenizing the frozen tissues in TRIzol reagent (ThermoFisher Scientific) using an Ultra-Turrax homogenizer , and purified using Qiagen RNeasy columns . RNA integrity was checked on chip analysis  according to the manufacturer's instructions. RNA was judged as suitable for array hybridization only if samples exhibited intact bands corresponding to the 18S and 28S ribosomal RNA subunits, and displayed no chromosomal peaks or RNA degradation products (RNA Integrity Number > 7.0).Total RNA (100 ng) was used for whole transcript cDNA synthesis and terminal labelling using the Ambion WT expression Kit . Samples were analysed on Affymetrix Bovine Gene 1.1 ST arrays . Hybridization, washing, staining and scanning of the arrays were performed automatically on an Affymetrix GeneTitan instrument, according to the manufacturer's instructions. Packages from the Bioconductor project , integraThe 553,263 probes on the Bovine Gene ST 1.1 array were redefined utilizing current genome information. In this study, probes were reorganized based on the gene definitions as available in the NCBI database , which represented 20,526 unique genes. Normalized expression estimates were obtained from the raw intensity values using the robust multiarray analysis (RMA) preprocessing algorithm available in the library ‘affyPLM’ using default settings .Differentially expressed probe sets were identified using linear models, applying moderated t-statistics that implemented intensity-based empirical Bayes regularization of standard errors (library ‘limma’). The moderated t-test statistic has the same interpretation as an ordinary t-test statistic, except that the standard errors have been moderated across genes . P-vA dataset containing 20,526 gene IDs with respective fold change and p-values was uploaded into the Ingenuity Pathway Analysis software (IPA). Core Analysis was performed using default settings with a p < 0.001 and FDR < 0.1 cut-off, which yielded a total 353 mapped genes ready for analysis.The Downstream Effects Analysis was used to determine which Molecular and Cellular Functions were most relevant to the DE genes dataset  and which functions were significantly predicted as decreased or increased .Upstream Regulator Analysis was used to predict which endogenous regulator genes  control the expression of downstream DE genes by measuring an overlap p-value (p < 0.05) with Fisher’s Exact test. Their predicted activation/inhibition state (z-scores ≤−2 or ≥2) was also verified based on the collective directional expression of their target genes. Pathway Designer tool was used to generate network illustrations derived from Downstream Effects Analysis (Fatty Acid Metabolism and Mitosis).The Regulator Effects Analysis (customized settings: max. 1 regulator and 1 function per network generated) was used to determine how the predicted active/inhibited Upstream Regulators impacted downstream DE genes and eventually sub functions. The Upstream Regulators, sub functions and their predicted states were used to generate a customized network to directly link Upstream Regulators states with affected functions in the cellular context. Data visualization was created using the Pathway Designer tool.BOS TAURUS genome assembly, the expression of a total of 19,968 genes was detected (miRNA genes excluded). Expression varied widely among genes due to treatments from very stable genes with expression fold changes (FC) lower than 1.001 up to genes with FC > 13 such as lipocalin-1 and 3-hydroxybutyrate dehydrogenase 1. However, for most genes (94.48%) expression varied less than 50% (FC < +1.5 or FC > -1.5) . Still, 1.98% of the genes (396) were considered differentially expressed, satisfying a cut-off level of p-value < 0.001 and FDR < 0.1.When remapping the probes to the . The analysis showed how DE genes are involved in a variety of biological processes. Their roles included several aspects of adipose cell metabolism, with the top functions ranging from Cell Cycle, which consists mainly of DNA replication and cell division, to Lipid Metabolism, considered the prime function of adipose tissue. The amount of genes involved in each function varied considerably, with the most in Cell Death and Survival (118 genes) and the least in Energy Production (14 genes).A total of 353 genes out of 396 DE genes  identified in this study were successfully mapped to the Ingenuity Knowledge Base (IPA). Of these, 220 were up-regulated and 133 down-regulated. Based on this set of genes, IPA Core Analysis revealed that genes differentially expressed due to dietary treatment were involved (p<0.05) in nine Molecular and Cellular Functions Table 2). Z-scores for sub functions were all positive (Z-score > +2), indicating functional increase. Fatty Acid Metabolism was the only sub function from Lipid Metabolism category that was significantly predicted to be increased and it was the process with the overall strongest activation prediction (Z-score = + 2.605) with a total of 26 DE genes involved. The directional expression of most of these 26 genes indicated that calves under the enhanced nutrient supply had a significant increase in Fatty Acid Metabolism function . This Downstream Effect Analysis result (Fatty Acid Metabolism increase) is based on the Ingenuity knowledge Base that indicated that out of these 26 genes, 15 genes showed a directional expression compatible with an increased function , 3 genes had expressions compatible with a decreased function  and 8 gene are involved in this function  but literature results, in Ingenuity Knowledge Base, are not consistent whether they increase or decrease it. .Data was further processed through Downstream Effects Analysis in order to predict which biological processes were increased or decreased based on the directional expression of the involved DE genes. A total of eight predictions of sub functions activation states were statistically significant (Z-score > +2). They derived from the following main Molecular and Cellular Functions categories: Lipid Metabolism, Small Molecule Biochemistry, Cellular Transport, Cell Cycle, and Cellular Assembly and Organization .Cell Cycle was the functional category most represented, with four sub functions significantly predicted to be increased: Interphase (30 DE genes), Cell Cycle Progression (43 DE genes), Entry into Interphase (10 DE genes), and Mitosis (23 DE genes). Ingenuity knowledge Base indicated that out of these 23 DE genes involved in Mitosis, 11 genes showed a directional expression compatible with an increased function , 2 genes (CDC20 and MET) had expressions compatible with a decreased function, and 10 genes  were involved in this function but literature results, in Ingenuity Knowledge Base, do not indicate whether they increase or decrease Mitosis (Table 3). This set of Upstream Regulators accounted for the molecular regulation of 108 out of 353 DE genes identified in this study. These transcriptional factors varied on how they influence DE genes regulation, from 4 DE genes by SMOC2 up to 22 DE genes by PPARG (Table 3).Upstream Regulator Analysis identified a total of 346 molecules as potential upstream regulators for the set of 353 DE genes, including transcriptional regulators, cytokines, ligand dependant nuclear receptors, G-protein coupled receptors, growth factors, kinases, transmembrane receptors, and others, but excluding drugs and chemicals. However, the analysis was able to reveal the activation status of only 23 Upstream Regulators based on the directional expression of their controlled DE genes . Most of them (13 genes) were predicted to be activated  with a Z-score higher than +2, while 10 were predicted to be inhibited  with a Z-score lower than -2 . By combining the activation status of the Upstream Regulators with the directional expression change of the genes they regulate and Ingenuity Knowledge Base information, this analysis provided evidence indicating that TCF3, SMARCE1, RABL6, BNIPL3, and PTGER2 were predicted to be involved in the regulating of Cell Cycle Progression. FOXM1, MED1, SMARCB1, NUPR1, and CSF2 were involved in Entry into Interphase. INSIG1, KLF15, and PPARGC1A were regulating Fatty Acid Metabolism. E2F3 was regulating Interphase related functions. SMOC2 was regulating Mitosis. PPARG was involved in Transport of Carboxylic Acid. AHR was predicted to be involved in Fibrosis of Liver aspects, however, this result was not included as samples were derived from adipose tissue. PPARA did not appear in any result of Regulator Effects feature .A total of 17 upstream regulators were further analyzed through IPA’s Regulator Effects feature in order to reveal not only what genes they are likely to control, but ultimately what functions the identified Upstream Regulators are affecting .Adipose tissue is a dynamic organ and plays a crucial role in energy balance. Several factors have been reported to impact the molecular regulation of adipose tissue in bovine such as age , depot lAs previously reported , calves Table 1). Adipose tissue seems to be highly responsive to dietary treatments. Transcriptome data from the same animals indicate that adipose tissue genes were more affected by the treatment compared to muscle and bone marrow[In this manuscript we further show that an enhanced nutrient supply also alters adipose tissue molecular regulation. The dietary treatment altered the adipose transcriptome molecular regulation as gene expression changed by more than 1.5 FC for more than a thousand genes . Cell Cycle appeared as the most significant function for the DE genes set. This function is related to the process of cell reproduction. Adipose tissue development happens through two distinct processes: hypertrophy (increase in cell size) and hyperplasia (increase in cell number) [White adipocytes are considered the main cellular type in adipose tissue; however this organ is also composed of other cells such as preadipocytes, brown adipocytes, beige adipocytes, macrophages, endothelial cells and stem cells . Therefo number) . The latTable 2) further revealed the predicted activation state of sub functions based on the directional expression of DE genes involved. Fatty Acid Metabolism, which belongs to the Lipid Metabolism category, was the function with the highest predicted activation state (z-score). This finding is based on the directional expression of the 26 DE genes involved , which indicates that adipose tissue of calves under an enhanced nutrient supply are processing fatty acids at a higher rate compared to restricted animals. Another study with similar treatments reported that the gene network ‘energy production, lipid metabolism and small molecule biochemistry network’ was enriched in subcutaneous adipose tissue of calves (126 day old) fed an enhanced diet [Downstream Effects Analysis (ced diet . Older aced diet .Table 2) further showed that four sub functions belonging to Cell Cycle category were predicted to be increased: Entry into Interphase, Interphase, Cell Cycle Progression, and Mitosis. These results indicate that omental fat in the calves under the enhanced nutrient supply experienced an increased tissue expansion through hyperplasia. However, hypertrophy might have also contributed to adipose tissue development since the sub functions Development of Cytoplasm and Formation of Cytoskeleton (Cellular Assembly and Organization category) were predicted to be increased. These findings are evidence that early life nutrient supply influences the genetic regulation of hyperplastic and hypertrophic processes in calves’ adipose tissue. Our findings are supported by another study investigating low vs. high plane of nutrition in calves’ subcutaneous adipose tissue histology and transcriptome [A higher nutrient intake allows adipocytes to increase fat stores by either an increased uptake of free fatty acids or de novo lipogenesis ,35. Thiscriptome . Histolocriptome .In another study, pre-weaned Friesian calves fed an enhanced vs. restricted nutrient supply displayed increased adipose cell hypertrophy, but hyperplasia did not differ at 95 days; however after weaning treatment diets were paired until slaughter at 553 days where no differences in adipose cellularity were observed , which mTable 3). Even though none of these 23 upstream regulators were differentially expressed, they are likely to orchestrate a significant part of the effects of the enhanced nutrient supply on adipose tissue regulation, since 108 DE genes were predicted to be under their control.The regulation of gene expression in bovine adipose tissue is complex. There is a multilayered system with different control levels to ensure that gene expression is fine tuned, including epigenetic modifications (e.g DNA methylation and histone modifications) , transcrFig 4). The dietary effects can be grouped mainly into Lipid Metabolism and Cell Cycle functions. PPARG, PPARGC1A, KLF15 and INSIG1 are known to affect not only Lipid Metabolism but also adipogenesis. PPARG is the master regulator of adipogenesis and regulates several aspects of Lipid Metabolism [The combination of customized Regulator Effects analysis results with a Network construction with exclusion of regulated genes provided a simplified view of the impact of the dietary intervention on Upstream Regulators and consequentially on Molecular and Cellular functions of adipose cells . Three transcriptional factors predicted as active  are known to be involved in the regulation of Interphase steps. E2F3 activates genes that control the rate of proliferation of cells [The other Upstream Regulators  were affecting functions related to Cell Cycle  . Another study also found that calves supplied with an enhanced diet display increased Letpin levels compared to calves in a restricted diet [The results of this study revealed that an early life enhanced diet changes adipose tissue physiology by increasing the metabolism of fatty acids and cell proliferation. Fat mass of calves from enhanced diet was likely to be increased as Leptin expression was higher  . This miIn conclusion, adipose tissue of pre-weaning female calves is developmentally and metabolically responsive to early life nutrient supply (enhanced vs. restricted). However, it is still not clear if it imprints adipose physiology and under what conditions it positively or negatively impact future animal performance. Additional studies considering both growing and productive stages will lead to a better understanding on how neonatal nutrient supply alters long term adipose function and how that relates to animal performance.S1 Table(DOCX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S1 File(DOCX)Click here for additional data file.


PPP1R3A emerges as a regulator whose network connectivity changes significantly between health and disease. RNA sequencing after PPP1R3A knockdown validates network-based predictions, and highlights metabolic pathway regulation associated with increased cardiomyocyte size and perturbed respiratory metabolism. Mice lacking PPP1R3A are protected against pressure-overload heart failure. We present a global gene interaction map of the human heart failure transition, identify previously unreported cardiac eQTLs, and demonstrate the discovery potential of disease-specific networks through the description of PPP1R3A as a central regulator in heart failure.Heart failure is a leading cause of mortality, yet our understanding of the genetic interactions underlying this disease remains incomplete. Here, we harvest 1352 healthy and failing human hearts directly from transplant center operating rooms, and obtain genome-wide genotyping and gene expression measurements for a subset of 313. We build failing and non-failing cardiac regulatory gene networks, revealing important regulators and cardiac expression quantitative trait loci (eQTLs).  PPP1R3A as a heart failure gene.The genetic and pathogenetic basis of heart failure is incompletely understood. Here, the authors present a high-fidelity tissue collection from rapidly preserved failing and non-failing control hearts which are used for eQTL mapping and network analysis, resulting in the prioritization of  Despite this, the underlying molecular mechanisms remain poorly understood and the few approved therapeutics target maladaptive compensatory pathology rather than proximate molecular mechanisms4.Heart failure (HF) is a life-threatening syndrome characterized by an inability of the heart to meet the metabolic demands of the body. HF costs the US more than $34 billion a year to treat 6 million patients12. These efforts have revealed changes in gene expression of key sarcomeric, calcium cycling, developmental and metabolic genes. Because of the significant logistical challenge of harvesting healthy hearts, few studies have included a nonfailing control group, making conclusions regarding the transition to HF tentative. Further, as gene expression programs are rapidly altered in an environment of high oxidative and nitrosative stress13, the high metabolic rate of the heart limits the utility of post-mortem tissue for gene expression analysis .With rapidly increasing access to high-throughput sequencing technology, molecular characterization of human heart tissue has become possible, and in recent years a number of efforts to define the regulatory transcriptional architecture of HF in humans and small animals have been undertakenPPP1R3A as a novel predicted HF regulator. Lastly, our in vitro and in vivo approaches further demonstrate its role in HF pathology.Thus, the expansion of this resource with high-fidelity tissue collection and molecular characterization is required, and a crucial next step in synthesizing this information is the identification, and in vitro and in vivo validation, of novel molecular actors in this disease. In this study, we identify previously undetected cardiac expression quantitative trait loci (eQTLs) from genome-wide genotyping and gene expression measurements from rapidly preserved failing and nonfailing human heart tissue. Condition-specific cardiac regulatory gene networks identify disease-driven changes in local and global topology and pathway organization, illuminating 17. Using this consensus protocol, we obtained 1352 human cardiac samples and chose a subset of 313 hearts, including 177 failing hearts collected immediately post transplantation and 136 healthy donor controls that were suitable for transplantation but did not reach a recipient due to logistical reasons  and to explore the genetic landscape of cardiac gene expressionNPPA and NPPB, depletion of SERCA2A, and a shift from MYH7 towards MYH6 expression—established signatures of HF . Our results suggested that our samples displayed comparable contractility-related gene expression but had significantly less oxidative-stress-related gene expression and less perturbation in other metabolic pathways  in order to limit inflation of correlation between low covariance gene pairs.We assessed the quality of these measurements in several ways. First, we found that disease status was the dominant source of variation suggesting no major confounding sources of variation , inverse covariance estimation 21), and mutual information (ARACNe22 and ZScore) (see Methods for details. All network constructions are available at 10.5281/zenodo.2600420). Each of these methods has specific advantages for different questions, e.g. JGL creates a sparser network with the specific intent of reducing representation of noncausal associations, whereas WGCNA relies on denser network topology to capture modules of genes with high likelihood of interaction. Since our downstream analysis required a top-down systems view of coexpression networks, and because we planned to prioritize genes based on the change in connectivity of networks between disease states, we chose to base our subsequent analyses on WGCNA-derived networks, which represent a robust tool for this purpose.24We inferred undirected, disease-state-specific gene coexpression networks. Gene regulatory network inference from coexpression is a challenging problem that no single method solves adequately in all contexts. Here, we constructed control- and HF-specific networks using methods that rely on gene coexpression 25, the HF modules had more diversity of signaling and metabolic annotations  and the muscle 6-phosphofructokinase PFKM in the HF network ; as expected these eQTLs showed proximity to known transcription factor binding sites and transcription start sites  regulatory annotation (716/1566 (46%) and 425/936 (45%) of variants, for failing vs. control, respectively) and/or predicted for transcription factor binding  in each cohort. First, we performed hidden covariate correction using the PEER package (see Methods). We used QTLtools to perform association testing for each cohort separately (see Methods) and performed a scan on the number of hidden factors to correct with PEER.tes Fig. . We then28, and also displays greater overlap with GTEx than those found in an independent cohort of patients with dilated cardiomyopathy10. This large overlap was encouraging, and since we control for known and hidden covariates, our difference in eQTLs compared to related studies may reflect the immediate tissue collection techniques we used, and the difference in disease status .This percentage of newly identified eQTLs is larger than on previous, related eQTL studies29. First, we did a simple, direct SNP overlap check with the GWAS variants . For HF eQTLs, this revealed 41 variants, among which we found 25 associations with sudden cardiac arrest, heart rate variability, and coronary heart disease among other diseases/traits, whereas 33 of the nonfailing control eQTLs had associations in the catalog, including QT interval and heart rate variability traits . We chose this coronary artery disease GWAS for comparison not only because it is one of few related to causes of HF that are appropriately powered for this high-resolution method, but believed it to be a reasonable disease surrogate for comparison based on the 36% of explanted failing hearts in our study that had undergone coronary artery bypass grafting . The genes TAS2R43−TAS2R46 had overlapping, high similarity matching probes, suggesting possible cross-hybridization, while the rest of the receptors’ probes were deemed independent by this analysis. These associations, prevalent in both cohorts, highlight a common module of G-protein-coupled receptors that have been previously observed to be expressed in the healthy and failing heart34, and that may play a role in the regulation of arrhythmia and contractility35.We went on to identify modules of coordinating genetic loci and associated networks of genes within these associations by finding nontrivial connected components (i.e. with more than three nodes) within the bipartite association graph of variants and genes, including WGCNA edges with weights larger than the median Fig. . NotablyIn summary, our heart transplant cardiac samples and inferred gene coexpression networks enabled us to find several previously unidentified cardiac eQTLs in the failing and nonfailing heart. Many of the eQTL variants were also associated with cardiac phenotypes in GWAS and some are associated with genes in highly connected parts of the coexpression network, suggesting coordinated regulation.Next, we used our network topology to identify and prioritize genes that were dynamically connected between the failing and control heart networks. Our goal was to identify genes whose network connectivity was increased in the disease state (i.e. HF), but specifically to pathways known to be relevant to the global control of HF mechanisms. We achieved this by ranking genes on two connectivity metrics: (i) differential local network connectivity for each gene between control and failing networks, and (ii) change in each gene’s connectivity globally to HF-relevant molecular pathways manually curated from KEGG and Reactome . This includes the myosin binding protein C3 (MYBPC3) which has previously been implicated in the Mendelian cardiac muscle diseases, hypertrophic cardiomyopathy and dilated cardiomyopathy37 , Supplementary Fig. After classifying each gene in this way, we focused on central coordinators , with one of the highest changes in GC between control and failing hearts 38, has not been previously associated with HF. To examine the importance of PPP1R3A to cardiomyocyte hypertrophy across cardiomyopathic etiologies, we also examined its importance in a cardiomyopathy pathway , and found that its differential connectivity to this pathway  are major molecular drivers of decreased respiratory glucose metabolism in HF via inactivation of pyruvate dehydrogenase42. This results in shunting of pyruvate away from the oxidative TCA cycle into lactate, thus decreasing energetic efficiency of glucose metabolism in cardiomyocytes. We therefore hypothesized that decreasing PPP1R3A expression would lead to liberation of respiratory metabolism, and found that siRNA-mediated knockdown of PPP1R3A leads to increased basal and maximal respiratory metabolism of pyruvate by NRVM as measured by oxygen consumption  and p < 0.01  Fig. Noting that many of the central coordinators significantly regulated by PPP1R3A on HF in vivo using a model of pressure overload, transaortic constriction (TAC). Compared to Ppp1r3a+/+, Ppp1r3a−/− mice were protected from TAC-induced left-ventricular (LV) dysfunction as measured by fractional shortening (p = 0.002 (ANOVA), Fig. Nppa and Nppb in the LV of Ppp1r3a−/− TAC vs. sham mice compared to the expected TAC-induced increase observed in Ppp1r3a+/+mice , Nppb: p = 0.008 (ANOVA)). No significant difference was found across groups in the ratio of Myh7/Myh6 expression .We then investigated the effect of Ppp1r3a−/− TAC and sham in cardiomyocyte size, while Ppp1r3a+/+ animals showed expected cellular hypertrophy after TAC ). After TAC, heart weight/body weight ratio varied significantly between groups, though TAC comparisons within genotype did not reach statistical significance by Bonferroni post test ).In accordance with these findings, there was no difference between We have constructed a comprehensive gene regulatory map of human heart failure (HF). This effort has been facilitated by a systematic approach to the collection of control and failing heart tissue from the operating rooms of cardiac transplant centers and the resulting measurements have allowed us to describe several previously unrecognized molecular features of HF. Notably, the network structure of HF differs markedly from that of nonfailing heart tissue. Specifically, we find that the control network has a large number of genes (2614) not associated with modules, whereas in the HF network, only 13 genes remained unassociated, providing evidence for increased connectivity in the HF network. Further, in the HF network, there is significant rewiring of genes to new processes Fig. , and an TAS2R G-protein-coupled receptor family, receptors typically associated with the sensation of taste but recently found to be variously expressed in cardiac tissue33. Further, these eQTLs were enriched for regulatory annotations, which were more prevalent in the failing heart cohort. Thus, these newly identified eQTLs are not only important for identifying potential regulatory DNA, but also novel molecular actors in HF that would not have been discovered in healthy tissue alone.The inferred networks also aided in the discovery of new eQTLs in the nonfailing and failing contexts. Notably, we found a greater number of eQTLs in the failing heart, half of which were not previously reported, but that were still implicated in higher phenotype associations in GWAS. In some cases, the expression of local subnetworks of genes were found to be associated with one locus, such as several members of the 44 and highlights a critical feature of coexpression networks: they capture the global complexity of regulation beyond individual changes in expression to identify genes pivotal in disease. Here, our strategy classified genes both by their local connectivity and their network distance to HF-relevant pathways  to identify PPP1R3A as a gene with a putative role in HF. PPP1R3A would not have been identified without this network approach, given that its own expression is not altered drastically in disease. Subsequent molecular investigations demonstrated its effect on other central coordinators between the control and HF networks as well as a deleterious effect on contractile function in the setting of pressure overload in vivo. Although this gene has not previously been associated with human cardiac disease, studies in both mouse and human have found that loss-of-function mutations in PPP1R3A manipulate metabolic pathways in skeletal muscle, and our own analysis implicated it in pyruvate, and other metabolic pathways  on the 17-segment model. On some of the occasions, when it was necessary to avoid infarct or peri-infarct tissue in these segments, we obtained tissues that may be closer to the base or apex or more anterior or inferior . The septum was never collected. Histopathology using H&E and trichrome staining was used to avoid the use of donor hearts with excess fibrosis or hypertrophy. Immunostaining was not performed.The institutional review boards at all collection sites  reviewed and approved the protocols used in this study for procurement and use of human tissue and information. All participants gave informed consent before enrollment.48.We performed RNA expression measurements and obtained genotype information in genome-wide markers for 313 patients  hearts) using Affymetrix expression and Affymetrix Human 6.0 respectively. Clinical variables for each individual were recorded during the course of the research and were compiled using REDCap49, and by correcting for observed covariates  using robust linear regression. The residuals after these corrections were then used as the gene expression values for downstream analyses. Previously to these corrections, the data was log-transformed to better resemble a normal distribution. Differential expression analysis was performed using the Significance Analysis of Microarrays (SAM)50. Genes shown in Supplementary Figure Several technical and sample covariates can bias gene expression values inferred from our microarray data, such as array batch effects and individual ethnicity, gender. These covariates can greatly confound downstream analyses, resulting in false positive and negative associations and reducing the power of statistical analyses. We corrected for these biases in three ways: by normalizing to “reference” probes , by applying batch normalization using ComBatn = 7960) and on each cohort separately, except in the case of the joint graphical LASSO (see below).19 on each network separately.WGCNA: We ran the WGCNA pipeline including correlation matrix, TOM transformation, and Dynamic Tree Cut module finding as prescribedabs(R) > 0.6.Pearson correlation: We obtained the empirical Pearson correlation matrix and performed an absolute correlation coefficient cutoff of ARACNe: We ran the method with default parameters and 500 bootstrap iterations for calculating significant gene coexpression relationships.A and B considering all interactions of A and B against each other gene.Z-score: Otherwise known as the CLR method, we ran the method using the Pearson correlation matrix as input. The output is then a statistical scoring of each interaction for genes Joint graphical LASSO (JGL): The JGL has two main parameters—a sparsity penalty to tune how many interactions are found and a group penalty that tries to match the network structure of the two cohorts. We performed a grid search on these parameters and chose the one that maximized the Akaike Information Criterion. This resulted in a very low group penalty of 0.01 and a modicum sparsity penalty of 0.1, which was applied to a standardized matrix of expression values for both cohorts.We ran the following methods on the covariate-corrected data described in the section above using the top 40% most variable genes 52. To find independent eQTLs, we performed LD-pruning  and provide a set of pruned variants in Supplementary Data p values before and after correction for age, gender and site are provided in Supplementary Fig. Prior to eQTL discovery, we used PEER to find hidden covariates that could confound signals in our data as well as filtering any genotypes with major allele frequencies less than 5%. To test associations between gene expression in each cohort separately, we used QTLTools29. We ran the eCAVIAR32 pipeline using the FINEMAP implementation on all loci with at least one SNP with p < 1e-5 in the GWAS and at least one SNP with p < 1e-5 in either condition of our eQTL study. We found evidence of colocalization at six genes: MRAS, TCF21, GPR22, LIPA, ZNF664, and EIF2B2. EIF2B2, TCF21, and ZNF664 colocalized in both failing and healthy hearts. However, LIPA colocalized only in healthy hearts, while GPR22 and MRAS colocalized only in failing hearts. These context-specific colocalizations highlight genes that may contribute to heart disease progression specifically in healthy (LIPA) or in already-failing hearts . LIPA codes for the lysosomal enzyme lipase A. GPR22 has previously been shown to play a protective role against myocardial53. The protein MRAS is a muscle-expressed homolog of the Ras oncogene family, currently without any well-characterized mechanism in coronary artery disease.We tested whether any of our eQTLs colocalized with the signals from a publicly available GWAS on coronary artery diseaseLC) of any gene G was calculated as the difference between max-normalized weighted network degree of G. The global connectivity metric (GC) of any gene G was calculated as the number of gene sets that were significantly differentially enriched between gene rankings of failing and control networks obtained by ordering the genes by their absolute correlation coefficient to G.The local connectivity metric  as the number of curated HF-relevant pathways within its neighborhood (defined by genes lying within a set rank of absolute edge weight to gene g) that were significantly enriched in g’s neighborhood by the following procedure  to g in both the HF and control networks. This resulted into two ranked lists of g’s neighbors: HF- and control network specific.For each pathway, we first ranked the neighbors of g, we plotted the number of network neighbors belonging to a curated list of known HF-relevant pathways from KEGG and Reactome  statistic  with a Benjamini−Hochberg correction .For each gene GC was then defined as the number of HF-relevant pathways found to be significantly enriched in such manner.A gene’s After inferring the gene coexpression networks for both cohorts, we calculated topological properties for each gene in each network in order to get a sense of a gene’s role in the networks and in the context of known pathways and gene sets. To this end, we defined a gene LC for a gene g used for this purpose was calculated as follows:g, net) is the max-normalized weighted degree of gene g in network net .To compare per-gene perturbations in local connectivity between HF and control networks, we defined the change in local connectivity metric LC and GC are then Z-score normalized in order to call coordinator status:LC and GC greater than zeroCentral Coordinators have Z-score normalized LC greater than zero but GC less than zeroLocal Coordinators have Z-score normalized GC greater than zero but LC less than zeroPathway Coordinators have Z-score normalized GC or LC greater than zeroNon-coordinators have neither Z-score normalized Both All use of animals in this study (including the below in vivo experiments) was reviewed and approved by the Stanford University School of Medicine Institutional Animal Care and Use Committee. NRVMs were isolated from pregnant Sprague−Dawley rats (Charles River) using standard collagenase protocols as described previously and cultured in serum-free DMEM media. In order to attenuate the effects of fibroblast contamination and accentuate metabolic changes, no glucose was added to the media and a final concentration of 20 μM of the fibroblast inhibitor Ara-C (Sigma-Aldrich) was incorporated. Furthermore, for robust expression measurements, at least 1 million cells were plated in a 12-well plate, corresponding to at least 70% confluency. For phenylephrine-treated cells, 50μM of phenylephrine was added 48 h after isolation. For the knockdown experiments, cells were transfected either with an siRNA targeted to PPP1R3A  or a scrambled siRNA using the RNAiMAX system (Invitrogen) according to the manufacturer instructions; transfections were performed 24 h after isolation. RNA extraction was performed using the Qiagen RNeasy kit according to the manufacturer’s instructions and were DNAse-treated using the DNA-free RNA kit from Zymo research. CDNA was synthesized with the high-capacity cDNA reverse transcription kit from ABI and qRT-PCR assays were performed using KAPA SYBR FAST on a ViiA 7 ABI system.For visualization, cells were fixed directly in the culture plate wells using 4% PFA in room temperature for 10 min, permeabilized with 0.2% Triton-X in room temperature for 10 min, and blocked with DAKO protein block in room temperature for 30 min. Afterwards, fixed cells were incubated in mouse anti-sarcomeric α-actinin antibody (Sigma) in DAKO antibody buffer overnight at 4 °C. Alexa secondary antibodies were added the next day after PBS washes and the cells were incubated for 1 h at room temperature. Cells were then washed with PBS and the nuclei were stained with DAPI . Fluorescent cells were then imaged at 40× or 20× magnification using an Olympus BX-51 inverted fluorescent microscope. Cell area was quantified with ImageJ.PPP1R3A knockdown efficiency and phenylephrine treatment using qRT-PCR prior to library construction. RNAseq libraries were prepared using the TrueSeq Stranded mRNA kit (Illumina), according to the manufacturers’ instruction. Libraries were barcoded, quality-checked using a 2100 BioAnalyzer and run in rapid run flow cells in a HiSeq 2500 (Illumina), producing at least 30 million paired-end reads.After RNA extraction, RNA integrity was checked using a 2100 BioAnalyzer (Agilent); all RNA samples had an RIN of 7.0 or higher. Samples were screened for Rattus Norvegicus rn5 UCSC reference genome using the STAR aligner55. Quantification and differential expression analysis of RNAseq data was performed using the Cufflinks package49: full transcriptome assembly was performed with Cufflinks, quantified with Cuffquant, and analyzed for differential expression using Cuffdiff. All genes deemed to be significantly up- or downregulated in the main text were called as differentially expressed by Cuffdiff.Sequencing reads were aligned to the −/− mice (C57Bl6 background) were a generous gift from Anna de Paoli Roach41. Ppp1r3a+/+ animals were C57Bl6 background (Jackson). All procedures involving animal use, housing, and surgeries were approved by the Stanford Institutional Animal Care and Use Committee (IACUC). Animal care and interventions were provided in accordance with the Laboratory Animal Welfare Act.Ppp1r3aPpp1r3a−/− and 10 Ppp1r3a+/+) were randomized to transaortic constriction (TAC) or sham surgery (five in each group). Animals underwent TAC as previously described50 at 10 weeks of age. Briefly, mice were anesthetized using an isoflurane inhalation chamber, intubated and ventilated. After surgical exposure of the thoracic aorta, a 6.0 silk suture was placed between the innominate and left carotid arteries to induce a constriction of ∼0.4 mm in diameter. In sham group mice, an identical procedure was conducted, without the constriction of the aorta. One week following TAC, gradients across constriction were measured and were not different between genotypes .Twenty male mice (10 50. Measurements occurred at 1 day prior to surgery (baseline), 7 days and 14 days after surgery and then every 14 days prior to euthanasia and tissue collection at 8 weeks after TAC.In vivo left-ventricular systolic function was evaluated by echocardiography in the short axis view as previously describedUpon euthanasia, heart weight, body weight and tibia length were measured by standard method  (uncoupler of oxidative phosphorylation). After OCR measurements were complete, viable cell number was assayed using PrestoBlue Cell Viability Assay (Thermofisher #A13261), and data were analyzed as OCR per cell.Seahorse technology  was used to measure oxygen consumption rate (OCR) 48 h after transfection. Cells were either exposed to base media or media including pyruvate immediately before experiment. Basal metabolism was measured first. Maximal respiration was measured 1 min after delivery of Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Reporting SummarySource Data


This is conclusively established via an extensive suite of both bulk and compound-specific radiocarbon dates.Bog butters are large white or yellow waxy deposits regularly discovered within the peat bogs of Ireland and Scotland. They represent an extraordinary survival of prehistoric and later agricultural products, comprising the largest deposits of fat found anywhere in nature. Often found in wooden containers or wrapped in animal bladders, they are considered to have been buried intentionally by past farming communities. While previous analysis has determined that Irish bog butters derive from animal fat, their precise characterisation could not be achieved due to diagenetic compositional alterations during burial. Via compound-specific stable isotope analysis, we provide the first conclusive evidence of a dairy fat origin for the Irish bog butter tradition, which differs from bog butter traditions observed elsewhere. Our research also reveals a remarkably long-lived tradition of deposition and possible curation spanning at least 3500 years, from the Early Bronze Age (c. 1700 BC) to the 17 Published radiocarbon determinations on Irish bog butters show activity spanning the Iron Age to the post-medieval period4 with folk accounts indicating survival into the 19th century6. While the reasons behind their deposition continue to be debated2, the remarkable preservative properties of peat bogs are well known7 and several post-medieval accounts mention the practice of storing butter in bogs to be consumed at a later date, whether by necessity or as a delicacy10. Early medieval Irish law tracts list butter as one of the products payable as food rents11, which may have needed to be stockpiled or stored. Parallels have also been drawn with the widespread deposition of metal and other objects in wetlands during the Bronze Age and Iron Age, often assumed to be votive or ritual acts14.Bog butters are large, white to yellow waxy deposits regularly recovered from the peat bogs of Ireland and Scotland, often found in wooden containers or wrapped in bark or animal membranes Fig. . With re19. Like bog butter, the chemical composition of adipocere is dominated by saturated fatty acids , with smaller amounts of unsaturated fatty acids , hydroxy fatty acids  and intact triacylglycerols22. In 2004, chemical analysis of nine Scottish bog butters using compound-specific stable carbon isotope measurements demonstrated six of the bog butters derived from a ruminant dairy source and three from ruminant carcass fat 23. Here we report on analyses undertaken on 32 Irish bog butters  in ruminant milk fats is in direct contrast to equivalent adipose fats, which contain very few fatty acids with chain lengths less than C14:026. Compared to other biochemical classes such as carbohydrates and proteins, the relative hydrophobic nature of lipids ensures their more frequent survival during archaeological timescales, with one of the most common finds being that of degraded animal fats27. By using high-temperature gas chromatography (HTGC) and GC/mass spectrometry (GC/MS), such organic remains can easily be classified as deriving from animal fats based on the identification and distribution of free fatty acids (mainly C16:0 and C18:0) and any remaining acylglycerols. Identification to species or fat type is much more difficult due to diagenetic transformations that occur during burial29. The heavier of the low molecular weight triacylglycerols, with acyl carbon numbers of C40 and C42, occasionally survive and can suggest a ruminant dairy fat origin rather than an adipose fat. However, dairy fat residues surviving archaeological timescales are often indistinguishable from adipose fat due to the increased solubility and hence preferential loss of the short chain fatty acids; for each additional methylene group of a fatty acid there is a fourfold decrease in its solubility  and non-ruminant animals (e.g. pigs), adipose tissue is the main site for the storage of lipids, with triacylglycerols being by far the most abundant constituent, making up over 95% of the total lipids present13C values) on individual fatty acids, distinctions between the adipose fats of different animals can readily be achieved, first demonstrated by comparing the δ13C values of lipid residues extracted from medieval dripping dishes and lamps with modern reference fats from pigs and ruminant animals32. Since then, traces of ruminant dairy fats  have been successfully identified in a large number of pottery vessels from throughout Europe and the Near East35.With the aid of stable carbon isotope determinations  were the principal lipid components, with palmitic (C16:0) and stearic (C18:0) acids predominating  ranged from C44 to C54 acyl carbons. Those with acyl carbon distributions ranging from C42 to C54 are entirely consistent with a degraded dairy fat origin. However, those with acyl carbons ranging from C44 to C54 are more consistent with a ruminant adipose fat source, although a dairy origin cannot be discounted due to the presence and abundance of the C44 triacylglycerol. A more reliable approach in the identification of the Irish bog butter origins was through the measurement of the stable carbon isotope values  of their C16:0 and C18:0 fatty acids. These values were compared against a global database of modern reference animal fats including animals from the UK raised on a pure C3 diet  representing ranges corresponding to reference non-ruminant adipose fats and ruminant adipose and dairy fats  of the bog butters with the reference fat values  could not be precisely classified as their Δ13C values plotted mid-way between the reference ranges for ruminant adipose and dairy fats. While no container was associated with IB1, both IB8 and IB21 were wrapped in animal bladders and their fatty acid δ13C values may have been altered by the lipid content of these wrappings. As with the Scottish bog butters23, where multiple samples were analysed from each bog butter mass, the homogeneity of bog butters and the robustness of the stable isotope methodology was confirmed. Analyses of sub-samples taken from the top (IB22tp) and bottom (IB22bm) of the same bog butter mass (IB22), revealed analogous results. Bog butter IB21, comprising two masses (IB21a and IB21b) wrapped separately, also provided very similar values.The δats Fig. . Twenty-ues Fig. , and her3 but shown here to be Middle Iron Age. It joins the straight-sided tubs from Glastonbury Lake Village as rare examples of prehistoric decorated wooden vessels38  used to consolidate wooden material3. Bog butter samples from Rosberry (IB18) and Teernakill (IB19) are both several centuries younger than their containers, although a sample from Rosmoylan (IB12) is a similar age to its associated wooden keg (Supplementary Table Alnus sp.) of the Rosberry keg is noted, none of the previously reported dates on wooden vessels include information on whether heartwood or sapwood was sampled40.Radiocarbon dating undertaken for this study has provided 50 new measurements on 32 Irish bog butters, adding to the 20 previously published measurements on 19 examples , and the Drinagh bog butter are both associated with bark, possibly a wrapping or container - a method of storage also evidenced in the Iron Age and Early Medieval period .Together with two recently dated samples (Supplementary Table 14. Results from our study bring to 20 the number of recorded Iron Age bog butters (out of 46 dated samples), supporting this first observation although more research is needed to elucidate their relationship to boundaries. More than half (11/20) are associated with vessels, which in Iron Age Britain is recognised as a common category of votive object and argued to be linked to wider symbolic practices around food and agricultural fertility45. It is uncertain if such symbolism permeated the Irish examples, although we note fewer wooden vessels are associated with bog butter in the following Early Medieval period.Previous work has highlighted an apparent clustering of bog butter deposition in the Iron Age, as well as a possible focus on political and/or natural boundaries11. It is generally included in food-rents, quantities ranging from the fist-size pats of butter listed in 7th/8th century law texts to the ‘yearlie twenty fower methers of butter, and fiftie methers of barlie’ exacted by Lisgole Abbey, Co. Fermanagh in 160946. Interestingly, texts do not mention the practice of depositing butter in bogs, although raiding of butter stores (imenna) is periodically recorded; food security must have been an issue for communities, with the storage of butter in bogs perhaps a wise precaution.Just under half of the bog butters examined are medieval in date . Both early and later medieval written sources contain extensive references to dairy products, and butter is often portrayed as a luxury or upper-class foodth–12th centuries AD) bog butters were found in wooden containers, the remainder associated with leather, bladder or bark. Written sources indicate bark was commonly used for storing butter, with the Irish word rúsc meaning both bark and butter container11. The meadar or mether, a distinctive quadrangular wooden vessel, appears in the Late Medieval period and is associated with two dated bog butters: Tawnagh Beg (IB10) and Goolamore, both from Co. Mayo  is one of the ‘container-type’ methers and its date of AD 1170–1280 provides the earliest known example of either vessel type.In terms of vessels, six of the thirteen Early Medieval  bog butters span the Irish post-medieval period (AD 1550–1850), with several 1713C values plotting between ranges expected for ruminant adipose and dairy fats. Deposition of butter in bogs in Ireland dates from at least the Early Bronze Age, a practice that may reflect intensification of a well-established dairying economy and thus increased likelihood of substantial surpluses of butter, a highly perishable but nutritionally valuable resource. Indeed, it may be that the burial of fats in the ground was much more widespread in antiquity than the archaeological record reflects. The survival of major hoards in bogs is consistent with their remarkable preservative properties; while burial of similar butter deposits in soil may well have achieved a similar goal of preservation in the short term, these deposits would not survive to the present day. The Irish bog butters thus provide a unique encounter with a vitally important agricultural product.Consistent with previous work, this investigation reveals that all sampled Irish bog butter were animal fats, which during burial had been diagenetically altered to resemble adipocere. GC-C-IRMS analyses of these substances revealed that twenty-six (81%) of the Irish specimens could confidently be assigned a ruminant dairy fat origin, with a further three samples  probably deriving from a ruminant dairy fat. Only three samples (9%) could not be identified to origin, with their δ23. Briefly, bog butter samples (ca. 1 mg) were extracted in a mixture of chloroform and methanol  via ultrasonication and then centrifuged, filtered and evaporated under a gentle stream of N2 to yield a total lipid extract (TLE). An aliquot of each TLE was trimethylsilylated using N,O-bis(trimethylsilyl) trifluoroacetamide  for analyses via HTGC and GCMS. Further aliquots were saponified using sodium hydroxide in methanol and double distilled water  and the fatty acids were converted into fatty acid methyl esters (FAME) using BF3/methanol  and analysed via GC-C-IRMS to determine their δ13C values, which were corrected for added derivative carbon via a mass balance calculation55. Bog butters were sampled from the middle of the mass, thus considered free of exogenous contaminant, and ca. 1.2 mg was directly weighed into tin capsules prior to graphitisation to obtain a bulk date. The compound-specific dates on single fatty acids were performed using preparative capillary gas chromatography for the isolation of single compounds39. Bog butter samples (bulk and single compounds) were graphitised into an Automated Graphitisation Equipment  and the resulting graphite measured on the BRIS-MICADAS instrument (Ion Plus) at the BRAMS facility in Bristol. Measurements were calibrated using OxCal v4.3.2 and the IntCal13 atmospheric curve57.Samples from 32 bog butters (IB1–32) from various locations throughout Ireland were supplied by the National Museum of Ireland, Dublin (Fig. Supplementary Tables S1 and S2