Allogeneic cell therapy products, such as therapeutic cells derived from pluripotent stem cells (PSCs), have amazing potential to treat a wide variety of diseases and vast numbers of patients globally. However, there are various challenges related to the manufacturing of PSCs in large enough quantities to meet commercial needs. This manuscript addresses the challenges for the process development of PSCs production in a bioreactor, and also presents a scalable bioreactor technology that can be a possible solution to remove the bottleneck for the large-scale manufacturing of high-quality therapeutic cells derived from PSCs. With their potential to cure a wide variety of disease indications and address vast patient populations, allogeneic cell therapies derived from pluripotent stem cells (PSCs) are poised to revolutionize therapeutic medicines ,2. HowevPSCs are mortal human cells that include specific cell types such as human embryonic stem cells (hESCs) and induced pluripotent stem cells (iPSCs). When cultivated in a 2D planar vessel, PSCs attach to a surface substrate and grow as a monolayer. In contrast, starting from single cells or small clumps in suspension bioreactors, PSCs that come into contact will naturally clump together to form spherical cell aggregates . The forDepending on a particular cell expansion and differentiation process, there is an optimal range of spherical cell aggregate sizes that can maximize the efficiency and production yield of expansion and differentiation processes in a bioreactor ,8,9. If There are varying published examples of optimal cell aggregate sizes for different cell types and cell culture process steps. One experiment using hESCs showed that maximum viability and minimal cell apoptosis during expansion was achieved when average aggregate diameter was 300 μm after 7 days of expansion, while apoptosis (with the majority of necrotic cells in the centers of aggregates) peaked by day 14 if the average diameter reached 500 μm . In anotWhile PSC aggregate formation can be influenced by variables such as cell proliferation rate, cell–cell adhesion strength, and cell packing density, the hydrodynamic environment inside a bioreactor, which is created by the impeller used to continually mix the liquid media, also has a significant impact on determining aggregate size and, ultimately, cell viability . The hydFluid mixing in a bioreactor using a traditional horizontal-blade impeller creates a significantly uneven hydrodynamic environment. The highest levels of shear stress and turbulent energy dissipation rates will be near the tips of the rapidly spinning impeller, with decreasing gradients of these hydrodynamic parameters as the distance from the impeller increases ,20. SuchIn contrast, the innovative Vertical-Wheel™ impeller, in conjunction with a distinct U-shaped vessel, provides a significantly more homogeneous hydrodynamic environment in Vertical-Wheel bioreactors. Computational fluid dynamics (CFD) analysis shows consistently low shear stress levels on all surfaces of the Vertical-Wheel impeller, as well as a narrow range distribution of turbulent energy dissipation rates throughout the U-shaped vessel  21]..21].Fluid mixing using the Vertical-Wheel results in a very low variation in these two hydrodynamic conditions while still achieving the complete and continual suspension of PSC aggregates. The homogeneous hydrodynamic environment will result in a much tighter distribution of PSC aggregate size A. As a rIn addition, the average diameter of PSC aggregates can be controlled by simply adjusting the agitation rate of the Vertical-Wheel impeller B. As agiBiological studies in Vertical-Wheel bioreactors have confirmed the inverse correlation between agitation rate and average size for iPSC aggregates A. The unAs previously mentioned, the uniformity of PSC aggregates is essential not only for the efficiency of cell expansion, but also for directed differentiation, by minimizing the chance that growth and differentiation factors diffuse unevenly through aggregates, and therefore avoiding the undesired heterogeneity and lower quality of target cells. In another set of experiments, iPSC aggregates were successfully differentiated into neural cells, which then formed cerebellar organoids in suspension within small scale Vertical-Wheel bioreactors . After 3While these various experiments were performed at relatively small scales (0.1 to 15 L) in Vertical-Wheel bioreactors, the homogeneous hydrodynamic environment created by the Vertical-Wheel impeller and U-shaped vessel has been modelled by CFD analysis up to 80 L scale and beyond. Therefore, it can be predicted that the formation of uniformly sized PSC aggregates will also be scalable in Vertical-Wheel bioreactors, resulting in efficient cell expansion and differentiation processes even at larger scales.A PSC expansion process can start with a small cryopreserved vial of PSCs from a working cell bank, inoculating thawed cells into a small-scale bioreactor, and then expanding the cells by serially passaging them into progressively larger bioreactors. Using the scalable bioreactor system with a consistent hydrodynamic environment will improve the efficiency of serial passaging and total cell yield. Studies were performed in order to determine the consistency of PSC expansion in Vertical-Wheel bioreactors, from small-seed culture scale (0.1 L) to potential production scale (80 L). Freshly thawed iPSCs were inoculated into a 0.1 L Vertical-Wheel bioreactor and then four consecutive passages of iPSCs were performed using scale-down model bioreactors of identical size. These studies repeatedly demonstrated that the use of Vertical-Wheel bioreactors led to an average 32-fold expansion of iPSCs during 6–8 days of culture period per passage, achieving a cumulative cell expansion of greater than one million-folds in 28 days . The celVarious scaling factors can be used to the determine potential scalability of expansion and differentiation processes developed at a small scale in Vertical-Wheel bioreactors. Common parameters that would need to be kept constant during scale-up include Reynolds number, impeller tip speed, power input, maximum shear stress, velocity, and energy dissipation rate. A recent study has suggested that maintaining the volume average energy dissipation rate, determined through computational fluid dynamics simulations, is the best scale-up method for predicting the agitation rates that will sustain a desired average aggregate diameter with narrow size distribution . TherefoThe ultimate goal of providing allogeneic PSC-derived products reliably to vast numbers of patients requires a series of optimized unit operations at various scales to meet target manufacturing lot sizes. Numerous manufacturing processes need to be considered, including seed train, expansion, differentiation, cell harvest, wash, concentration, fill-finish, and cryopreservation. In particular, upstream cell culture processes represent the most challenging bottleneck to achieving robust and economical manufacturing at larger scales. With homogeneous hydrodynamic environments and unparalleled scalability, Vertical-Wheel bioreactors can serve as the innovative manufacturing platforms to enable commercial-scale production of PSC-derived therapeutic cells and to secure the supply of these novel therapeutic cell products to patients.


Structural variants (SVs) may be an underestimated cause of hereditary cancer syndromes given the current limitations of short-read next-generation sequencing. Here we investigated the utility of long-read sequencing in resolving germline SVs in cancer susceptibility genes detected through short-read genome sequencing.Known or suspected deleterious germline SVs were identified using Illumina genome sequencing across a cohort of 669 advanced cancer patients with paired tumor genome and transcriptome sequencing. Candidate SVs were subsequently assessed by Oxford Nanopore long-read sequencing.TSC2.Nanopore sequencing confirmed eight simple pathogenic or likely pathogenic SVs, resolving three additional variants whose impact could not be fully elucidated through short-read sequencing. A recurrent sequencing artifact on chromosome 16p13 and one complex rearrangement on chromosome 5q35 were subsequently classified as likely benign, obviating the need for further clinical assessment. Variant configuration was further resolved in one case with a complex pathogenic rearrangement affecting Our findings demonstrate that long-read sequencing can improve the validation, resolution, and classification of germline SVs. This has important implications for return of results, cascade carrier testing, cancer screening, and prophylactic interventions. Next-generation sequencing (NGS) is the most widely used sequencing technology, and is based on the generation of short (50–300 bp) reads that are aligned to a reference genome or assembled into longer contiguous sequences (contigs) prior to alignment. Accurate alignment and variant calling in NGS is challenging due to regions of low sequence complexity, repetitive elements, and strong GC bias in the human genome, reducing the sensitivity and specificity for novel variant discovery. This indicates a need for improved approaches to characterize genetic variation, particularly for large or complex variants.A significant amount of genetic variation in the human genome is due to structural variants (SVs), such as deletions, duplications, inversions, and translocations.3 Recently, long-read sequencing (LRS) has been used to characterize complex genetic variation in human genomes and aid in the diagnosis of rare disorders.5 To investigate the contribution of germline SVs to cancer susceptibility, we used short- and long-read genome sequencing to elucidate pathogenic germline SVs in advanced cancer patients enrolled in a genomics-based precision medicine program. Here, we describe the application of nanopore sequencing to correctly interpret and classify SVs that could not be resolved through short-read genome sequencing.Moderate- to high-penetrance germline variants in cancer predisposition genes underlie 5–10% of all cancers. However, the prevalence of SVs in clinical and research cancer cohorts is likely underestimated due to the technical and computational limitations of multigene panel sequencing, exome sequencing, and genome sequencing.This study was approved by the University of British Columbia Research Ethics Committee, and written informed consent was obtained for all participants .6 Putative SVs were identified in genomes aligned to the human reference genome version hg19 using multiple copy-number and SV calling tools  program (NCT02155621).9 Variant calling was performed for samples sequenced on the PromethION using Sniffles v1.0.11.10 Paired tumor genome sequencing and transcriptome sequencing (RNA-seq) were assessed for somatic variants, loss of heterozygosity (LOH), somatic single-nucleotide variant (SNV) signatures, alternative splicing, and fusion transcript expression as previously described , of which a single copy could be mapped to the PromethION reads . Short-read genome sequencing demonstrated 100% sensitivity in variant detection for five known carriers, and identified pathogenic and likely pathogenic variants in five additional cases without prior genetic diagnoses. However, Illumina genome sequencing was insufficient to accurately and fully resolve 5 of 12 unique SVs, including two likely benign variants.The average human genome contains approximately 2500 SVs, including balanced rearrangements such as inversions and translocations, and unbalanced rearrangements such as large deletions, duplications, and insertions.5 Insertions, balanced SVs, and complex rearrangements that consist of three or more breakpoints are particularly difficult to characterize using NGS given the inferential nature of SV detection through contig-, split read-, flanking read-, or depth of coverage–based approaches. Although breakpoints of LINE-mediated variants remain difficult to assess by both sequencing technologies, long reads may span the entirety of homologous sequences or capture multiple breakpoints to inform haplotype configuration. For example, LRS helped resolve a single-exon inversion in RAD51C that would have been missed through targeted NGS and whose 5’ breakpoint was incompletely determined by short-read genome sequencing. As demonstrated by Rhees et al., the precise characterization of SV breakpoints is critical to guide the development of targeted clinical assays for familial, recurrent, or founder variants that may be undetectable through standard clinical assays in known or suspected hereditary cancer families.16Recently, LRS has allowed the molecular diagnosis of SVs causing Mendelian disease in cases where clinical assays or short-read genome sequencing have been unsuccessful.17 The significance of accurate variant interpretation, particularly in individuals who do not meet phenotype-based criteria, was highlighted by case 3 who was referred for clinical testing on the basis of the miscalled inversion in TSC2 and LOH in their tumor. At the time of referral, polymerase chain reaction (PCR)–based validations of the predicted breakpoint junctions were unsuccessful; however, LRS later characterized the true variant as a small inverted duplication in a deep intronic region of IFT140. On the basis of accurate variant resolution, classifications for this variant and a complex rearrangement at the locus of NSD1 were downgraded to likely benign. This ultimately prevented clinical referral for two cases without suspicious personal or family medical history.Although many carriers in our cohort had a personal and/or family history suggestive of moderate- to high-penetrance cancer susceptibility, 4 carriers (40%) did not have a previous personal or family history indicating referral for genetic counseling and testing Table . This fi13 This was exemplified by two complex rearrangements that could only be resolved through LRS, and one false-positive inversion that was refractory to accurate interpretation based on short-read sequencing. Recent studies have further shown the potential of amplification-free target enrichment for the sensitive detection of small variants and SVs at increased coverage and reduced costs.18Genome sequencing allows unbiased characterization of SV breakpoints, unlike relative and targeted approaches such as MLPA and NGS panels, that are influenced by variable efficiency in primer binding, probe hybridization, and target amplification. Despite the current limitations of LRS, including the necessity for high molecular weight DNA, higher error rate, and increased cost, this technology is particularly beneficial in the genetic diagnosis of monogenic disorders where NGS has failed to identify a causal variant. Many nonrecurrent SVs result from template switching between homologous repetitive elements, which are inherently difficult to map with short reads. As both we and others have shown, such variants are inaccurately or incompletely captured by NGS.As clinical genome sequencing becomes more widely used for molecular diagnoses in a variety of genetic syndromes, there is a need for standardized guidelines for the identification and validation of SVs using high-throughput sequencing technology. Considering the limitations of NGS, LRS offers a complementary approach in the diagnostic odyssey of patients and families where standard clinical testing is uninformative.Supplementary Information


Itch, in particular chronic forms, has been widely recognized as an important clinical problem, but much less is known about the mechanisms of itch in comparison with other sensory modalities such as pain. Recently, considerable progress has been made in dissecting the circuit mechanisms of itch at both the spinal and supraspinal levels. Major components of the spinal neural circuit underlying both chemical and mechanical itch have now been identified, along with the circuits relaying ascending transmission and the descending modulation of itch. In this review, we summarize the progress in elucidating the neural circuit mechanism of itch at spinal and supraspinal levels. Itch is an important somatosensation, but the central mechanisms underlying it are not fully understood. Here, Chen and Sun review recent studies on the spinal and supraspinal circuits involved in itch processing. Itch serves as an important protective mechanism that allows an animal to detect harmful substances invading the skin and remove them by scratching. The resultant scratching behavior, which is driven by strong emotional and motivational components, can sometimes induce a pleasant feeling, leading to an itch-scratch cycle. This itch-scratch cycle can result in serious skin damage for patients with chronic itch2.Itch is defined as an unpleasant sensation that evokes a desire to scratch and consists of sensory, emotional, and motivational components5.Itch, like many other somatosensations, typically originates from the skin. The itch signals are relayed by peripheral sensory fibers to the spinal cord, where the information is processed by local interneurons before reaching the spinal projection neurons. The spinal projection neurons then send the itch signals to the brain via the ascending pathways, and further processing of itch sensation occurs in multiple brain areas and circuits6. Progress has been made in deciphering the molecular and cellular mechanisms of itch in the peripheral nervous system. Several key receptors, including members of the Mas-related G-protein-coupled receptor (Mrgpr) and serotonin receptor families, were found to be important for detecting chemical itch signals12. It was shown that MrgprA3 marks a group of itch-selective neurons in the dorsal root ganglia (DRG)10. In addition, transient receptor potential (TRP) channels have been shown to be recruited by histamine-dependent and histamine-independent itch pathways in the periphery for itch signal transduction14. Consistently, mutation of the TRP channels causes pathological itch15. More recently, it has been shown that sodium channels expressed in primary sensory neurons also play important roles in itch signal transmission17. These developments on the peripheral mechanisms of itch have been discussed in several excellent reviews19.Based on peripheral inputs, itch can be classified into mechanical and chemical itch. Chemical itch can be further classified into histamine-dependent and histamine-independent subclasses according to the response to antihistamine agentsUsing genetic, pharmacogenetic and optogenetic approaches, recent studies have also begun to dissect the itch circuitry within the central nervous system. In this review, we will focus on these central circuit mechanisms that contribute to the sensation of itch. Note that although it is not possible to determine if an experimental animal feels the sensation of itch, for the purposes of this review, we interchangeably use the terms itch and scratch for animal studies.21. Recent studies have identified the molecular markers for excitatory neurons involved in the processing of chemical itch. Spinal neurons expressing gastrin-releasing peptide receptor (GRPR) are predominantly excitatory neurons, representing a key component of the spinal itch circuit , which marks a significant percentage of projection neurons in the rodent spinal cord23. Consistently, studies have shown that there is limited overlap between spinal GRPR+ neurons and spinal projection neurons labeled by retrograde tracing from the ventral posterolateral or ventral posteromedial nucleus (VPL/VPM) of the thalamus or lateral parabrachial nucleus (LPBN)25. Moreover, it has been shown that spinal GRPR+ neurons form excitatory synapses with spinal projection neurons, which convey the itch signal to the brain for further processing26. Thus, GRPR+ neurons represent a subpopulation of excitatory interneurons in the dorsal spinal cord that selectively transmit itch information.Multiple subtypes of excitatory neurons in the spinal cord are involved in itch processing. Genetic deletion of the transcription factor Tlx3 or testicular orphan nuclear receptor TR4 causes the loss of excitatory neurons in laminae I and II of the dorsal spinal cord, leading to significantly impaired pruritogen-induced scratching behavioruit Fig. 22,23. Th22, suggesting that GRPR is necessary for itch at the spinal level. Using slice electrophysiology and optogenetics, Pagani et al. further explored the mechanism underlying GRP/GRPR neurotransmission27. They found that single action potentials in presynaptic GRP+ neurons were not sufficient to evoke action potentials in postsynaptic excitatory GRPR+ neurons, although these two neuronal types were monosynaptically connected via glutamatergic synapses at a very high connection rate. Only when the GRP+ neurons were activated in burst firing mode could suprathreshold activation of postsynaptic GRPR+ neurons be achieved, and this depolarized or active state of GRPR+ neurons persisted for minutes even after the presynaptic optogenetic stimulus was terminated. Furthermore, the progressive depolarization of GRPR+ neurons depends on GRPR but not glutamate receptor signaling, as blocking GRPR almost completely abolished the suprathreshold activation of GRPR+ neurons induced by burst-like stimulation of GRP+ neurons, while antagonizing the glutamate receptors did not.Mice lacking GRPR show impaired responses to different types of pruritogens28. Early studies using immunohistochemistry showed that GRP was expressed in a subset of DRG neurons29, and the expression level of GRP was upregulated in DRGs in chronic itch models31. Dorsal root rhizotomy almost completely abolished GRP immuno-signals in the ipsilateral spinal cord30, confirming that the spinal GRP+ signals mainly originated from DRGs. However, some studies failed to detect GRP immunostaining signals in the DRG; instead, they found abundant GRP expression in the spinal cord. In addition, neither dorsal rhizotomy nor ablation of spinal cord TRPV1+ terminals altered GRP immuno-signals in the spinal cord28. Measuring GRP mRNA level by qPCR and ISH also confirmed that GRP was localized in the superficial dorsal spinal cord but not in the DRG32. Other groups further reported that GRP mRNA was barely detectable in DRGs of both juvenile and adult mice; instead, the majority of GRP is synthesized locally in the dorsal spinal cord33. Moreover, directly labeling the GRP+ neurons by using the GRP-EGFP or GRP-Cre transgenic mouse line confirmed the existence of GRP+ neurons in the dorsal spinal cord but not in the DRG37. Transcriptional profiling of DRG neurons using qRT-PCR or RNA-seq has revealed in an unbiased manner the molecular diversity of neurons underlying different somatosensation, yet GRP was not detected in these primary sensory neurons in several cases39, further supporting the idea of GRP expression in the spinal cord rather than in the DRG.Despite the essential role of GRP/GRPR in itch transmission, there has been long-standing controversy about the origin of GRP+ signals in a subset of DRG neurons as well as in the spinal cord40. Conditional deletion of GRP in primary sensory neurons significantly attenuated itch behaviors, supporting the idea that GRP expression in primary sensory neurons is essential for itch signaling. This study also showed that there are GRP+ neurons in the dorsal spinal cord, the ablation of which does not affect itch or pain responses. Optogenetic activation of GRP+ fibers in the skin was found to evoke itch-like scratching behavior, further verifying the existence of GRP+ neurons in the DRG, as well as their functional role in itch transmission40. Taken together, these data strongly suggest that GRP exists in spinal excitatory interneurons, as well as peripheral sensory neurons, both of which act upon downstream spinal GRPR+ neurons for itch transmission. The differential observation of GRP expression in the DRG in different studies may have resulted from the different efficiency and specificity of GRP-Cre mouse lines, since different strategies were used to generate these mice. Nevertheless, the question of whether GRP is expressed in the DRG has yet to be fully resolved.However, a recent study in which a GRP-Cre mouse line was generated using a knock-in strategy showed Cre+ neurons has been extensively studied. Ablation of spinal GRP+ neurons labeled in a GRP-Cre transgenic mouse line reduced pruritogen-induced scratching behavior, while pharmacogenetic activation of these neurons directly elicited spontaneous scratching behavior and enhanced the behavioral responses to various pruritogens36. By contrast, these manipulations caused no significant effect on the behavioral responses to acute noxious stimuli, emphasizing the selective role of spinal GRP+ neurons in itch. Consistent with the properties of the GRP/GRPR signaling pathway, in vivo optogenetic stimulation of spinal GRP+ neurons in burst mode, but not low-frequency stimulation with single pulses, elicited itch-like aversive behavior in freely moving mice27. Consistent with these findings, Sun et al. also showed that ablation of spinal GRP+ neurons decreased itch responses. However, they found that manipulation of spinal GRP+ neurons also affected pain responses37. Despite these conflicting results regarding the role of spinal GRP+ neurons in pain, which could largely result from the different experimental approaches for manipulating this neuronal population in these two studies, they both suggest that spinal GRP+ neurons play a critical role in spinal itch transmission  receptor (Npra)-expressing neurons in the dorsal spinal cord also selectively eliminates the itch responses induced by pruritogens, suggesting an important role of Npra+ neurons in itch processing. This manipulation suppresses Nppb-evoked scratching behavior but does not affect scratching behavior evoked by intrathecal injection of GRP. Moreover, Npra is coexpressed with GRP in a subset of spinal interneurons32. These data suggest that Npra+ neurons are located upstream of GRPR+ neurons along the spinal itch pathway , which is expressed in both the primary sensory neurons and the spinal cord. Mice lacking SST in both the peripheral and spinal cord exhibit behavioral deficits in response to pruritic stimuli. In addition, activation of the SST receptor Sst2a in the spinal cord enhances itch responses evoked by Nppb or GRP44. Dysregulation of spinal inhibitory interneurons could lead to hyperactivity of the itch circuit. This is seen from studies of mutant mice lacking the atonal-related transcription factor Bhlhb5, which is transiently expressed in the dorsal spinal cord45. These mice exhibit excessive scratching behavior and elevated itch responses to different types of pruritogens. Mice lacking Bhlhb5 were found to exhibit a dramatic loss of spinal interneurons expressing galanin and neuronal nitric oxide synthase (nNOS), but the distribution of two other inhibitory neuronal populations, expressing neuropeptide Y (NPY) or parvalbumin (PV), remained intact46, suggesting that spinal galanin+ and/or nNOS+ interneurons play a key role in gating chemical itch. Indeed, a more recent study examined the functional role of spinal galanin+ and nNOS+ neurons in itch signal processing and found that galanin+ neurons form predominant inhibitory synapses with spinal GRPR+ neurons and play an important role in gating chemical itch. By contrast, the functional role of spinal nNOS+ neurons in itch signal processing remains elusive, given the heterogeneous composition of this molecularly defined neuronal population, as well as their complicated synaptic connection with GRPR+ neurons47.Spinal inhibitory interneurons play a prominent role in regulating the spinal itch circuitryVglut2) in Nav1.8-expressing nociceptors resulted in pain deficits and enhancement of itch responses48, supporting the notion that pain can suppress itch at the spinal level49. Consistently, electrophysiological results in the monkeys have shown that scratching suppresses the neural activity of spinal neurons induced by pruritogens50, indicating an interaction between pain and itch at the spinal level. This finding is further supported by a recent study indicating that local inhibitory neurons might be involved in the interaction between itch sensation and other somatosensory stimuli in the spinal cord of the rodents51. The Bhlhb5+ interneurons likely receive direct synaptic input from primary sensory neurons responding to nociceptive and cooling stimuli46. Thus, Bhlhb5+ interneurons are well positioned to mediate the suppressing effect of pain on itch.Furthermore, genetic deletion of vesicular glutamate transporter type 2 46. It was shown that kappa opioid receptor (KOR) agonists selectively inhibit itch-induced scratching behavior but not pain-related behaviors, while blocking KOR signaling by KOR antagonists increases pruritogen-induced itch responses. These results suggest that spinal Bhlhb5+ interneurons inhibit itch through kappa opioid signaling and that DYN acts as an important neuromodulator of itch transmission  and/or glycine to tightly control the activity of postsynaptic neurons. Pharmacological activation of spinal α2 and α3 GABA57. Mechanical itch is independent of spinal GRPR+ neurons58, indicating that chemical itch and mechanical itch are processed by independent neural circuits at the spinal level.In addition to the chemical itch evoked by various pruritogens, itch sensation can also be evoked by light tactile stimulation, known as mechanical itch. In the periphery, mechanical stimuli are transduced and transmitted primarily by Merkel cells and Aβ primary sensory fibers, and the mechanosensitive Piezo channels expressed in Merkel cells functioning as the key mediator of mechanosensation59. Ablation or pharmacogenetic inactivation of Ucn3+ neurons significantly attenuates the scratching behavior evoked by light touch stimuli but not by pruritogens. Consistent with the critical role in mechanical itch transmission based on behavioral experiments, Ucn3+ neurons receive synaptic input from peripheral myelinated Toll-like receptor 5 (TLR5+) low-threshold mechanoreceptors. Another subpopulation of spinal excitatory neurons, which express the neuropeptide Y1 receptor (NPY1R), was also shown to be critical for mechanical itch60. These neurons receive extensive input from cutaneous low-threshold mechanoreceptors. Ablation or pharmacogenetic silencing of spinal NPY1R+ neurons reduces mechanical itch-dependent scratching behavior but not scratching behavior evoked by pruritogens60. Consistently, activation of spinal NPY1R+ neurons increases light touch-induced as well as spontaneous scratching behaviors, which are both GRPR+ neuron-independent. Thus, NPY1R+ neurons are largely specialized for transmitting mechanical itch information. Interestingly, there is only partial overlap between NPY1R+ neurons and Ucn3+ neurons in the dorsal spinal cord59. However, another study found that intrathecal administration of a NPY1R agonist attenuated the scratching behavior evoked by both mechanical and histamine-dependent chemical itch61, indicating a partially overlapping pathway for the transmission of these two different itch submodalities. These results emphasize the complexity of the spinal circuits underlying mechanical itch and chemical itch.Recent studies have identified several key components of the neural circuit for mechanical itch. It has been shown that spinal excitatory interneurons expressing urocortin 3 (Ucn3) represent a key node of the neural circuit for mechanical itch58. Ablation of spinal neurons labeled by neuropeptide Y (NPY) was found to induce spontaneous scratching behavior, which could not be blocked by the depletion of spinal GRPR+ neurons. Moreover, pharmacogenetic inactivation of spinal NPY+ neurons also enhanced light touch-induced scratching behavior58. These results support the idea that NPY+ inhibitory neurons in the spinal cord play a key role in gating the neural circuit responsible for mechanical itch. Consistently across studies, it has been shown that NPY+ neurons form functional inhibitory synaptic connections with NPY1R+ neurons and Ucn3+ neurons in the dorsal spinal cord60, confirming that NPY+ neurons gate the mechanical itch circuit at the spinal level  neurons in primates67, suggesting that histamine-dependent and histamine-independent itch are processed by distinct pathways.Spinal projection neurons, which target multiple brain regions, serve as a key relay for sending various somatosensory information to the brainals Fig. . The tra70, which is the receptor for substance P. Ablation of spinal NK1R+ neurons by intrathecal application of substance P-saporin significantly reduces scratching behavior evoked by pruritogens as well as chronic itch72. Interestingly, ablation of spinal NK1R+ neurons does not affect mechanical itch responses60, supporting the segregation between chemical and mechanical itch pathways. Elimination of spinal NK1R+ neurons also attenuates behavioral responses to noxious stimuli73, suggesting that nociceptive signals are also conveyed by these projection neurons. Electrophysiological recording revealed that primate STT neurons respond to peripheral itch, pain, mechanical, and thermal stimuli67, indicating the polymodal properties of spinal projection neurons. Information from different somatosensory modalities is thus processed and integrated by spinal projection neurons43.The functional role of spinal projection neurons has also been explored with behavioral experiments in rodents. The majority of projection neurons in the superficial dorsal horn express the neurokinin 1 receptor (NK1R)74, similar to the response properties of trigeminothalamic tract (VcTT) neurons. Unlike VcTT neurons, the VcPbT neurons showed a delayed peak as well as a prolonged response pattern after application of 5-hydroxytryptamine (5-HT)74, which better matched the time course of behavioral responses to pruritogens in awake animals. It was recently discovered that the spinoparabrachial pathway is activated by peripheral pruritic stimuli and that optogenetic inhibition of this pathway attenuated pruritogen-induced scratching behavior26, suggesting that the spinoparabrachial pathway plays a critical role in processing itch information. The parabrachial nucleus (PBN)-projecting spinal projection neurons receive direct synaptic inputs from spinal GRPR+ neurons26, suggesting that itch signals are transmitted from GRPR+ neurons to the PBN via a disynaptic circuit. Thus, PBN serves as a first central relay for itch transmission in the brain.Electrophysiological recording experiments in the rats also demonstrated that the majority of trigeminoparabrachial tract (VcPbT) neurons could be activated by various pruritogensAs mentioned earlier, ascending transmission of distinct somatosensory modalities, i.e., pain and itch, relies on spinal projection neurons. The interaction between pain and itch is widely distributed along sensory pathways, and there has been limited evidence directly examining the functional roles of spinocerebral projections in pain and itch simultaneously. It is thus possible that pain and itch recruit a similar population of spinal projection neurons. Whether these projection neurons can be further genetically and functionally divided into subclasses remains to be explored.92. Despite some differences in the brain regions revealed by different studies, most studies have found activation of the thalamus, primary and secondary somatosensory cortex (S1 and S2), prefrontal cortex (PFC), anterior cingulate cortex (ACC), insular cortex, premotor and motor cortex, and parietal cortex. These distinct brain areas are thought to be involved in different aspects of itch signal processing92. The thalamus is typically recognized as the relay station for somatosensation from the spinal cord to the primary sensory cortices, and the somatosensory cortex is proposed to encode the spatial, temporal, and intensity aspects of itch sensation92. Motor areas are thought to be involved in the planning and execution of itch-induced scratching behavior. Higher-order cortices, including the PFC and ACC, are likely involved in processing the emotional or motivational components of itch sensation. In rodents, by anterograde tracing initiated from itch-selective spinal GRP+ neurons36, several brain areas, including the thalamus, PBN, amygdala, S1, periaqueductal gray (PAG), and rostral ventromedial medulla (RVM), have been suggested to be itch-related brain areas. However, the functional role of most of these brain areas in itch remains to be examined.The representation of itch signal processing in the brain in earlier studies was mostly investigated by macroscopic imaging approaches, including positron emission tomography (PET) scans and functional MRI (fMRI). These functional imaging studies in humans have identified many brain regions that are activated by pruritic stimuli, as well as by itch-associated scratching or emotional changes87. Histamine-independent itch induced by cowhage more extensively recruits the insular cortex, claustrum, basal ganglia, thalamic nuclei and pulvinar. Histamine-dependent and histamine-independent itch are thus processed by largely overlapping yet distinct neural networks. The brain activity patterns responding to itch and pain are very similar88. No itch-selective brain region has been identified in most studies, suggesting that the differences between itch and pain signal processing possibly exist at the cellular level.Histamine-dependent and histamine-independent itch activate slightly different brain regions, although a series of core brain areas are activated by both kinds of itch, as revealed by human functional imaging studies5. Electrophysiological studies, however, have directly challenged this theory by showing multimodal response properties of neurons at different stages along the sensory pathways to different types of stimuli93. Instead, these data point toward a population coding mechanism that underlies the generation of the specific quality of somatosensation95. It is challenging to reconcile the data obtained in behavioral and electrophysiological experiments, since the experimental conditions, animal species, investigative strategies and approaches were all discrepant in different studies, yet it is hard to resist the idea that parallel pathways do exist to process specific somatosensory information, especially at the level of DRG and spinal interneurons. Given the complex interactions between itch and pain, or between other somatosensations at multiple neural levels, plus the fact that similar brain structures and neural networks could be recruited by both itch and pain, population coding might be a more economical and efficient way to differentiate diverse sensory modalities in the central nervous system, especially at the level of spinal projection neurons and higher brain centers, although we cannot completely exclude the possibility that dedicated labeled lines are retained at the supraspinal level.Molecular and cellular research strongly supports the existence of labeled lines, at least at the level of peripheral sensory fibers and spinal cord interneurons, by which different somatosensory modalities are processed by specific receptors, cells or neuronal circuits26. Furthermore, selective knockout of the glutamate transporter Vglut2 in the PBN significantly decreases the scratching behavior evoked by various types of pruritogens, as well as the scratching behavior in an allergic itch model, which suggests that PBN glutamatergic neurons play an important role in itch processing and further verifies the essential role of PBN in processing the itch signal  during itch-induced scratching behavior98 and found that DA fibers in the lateral shell of the NAc (NAc LaSh) showed more enhanced activity after scratching onset than in the medial shell of the NAc, suggesting that the VTA-NAc LaSh projection might be more prominently involved in itch-induced scratching behavior.Dopaminergic (DA) neurons in the ventral tegmental area (VTA) play a key role in encoding motivational drive91. This phenomenon is referred to as contagious itch. Previous fMRI studies have found that visually induced itch activates many brain regions that are typically involved in the processing of physical itch91, including the primary somatosensory cortex, insular cortex, and prefrontal cortex.Watching others scratching themselves or even talking about itchiness can induce a desire to scratch in humans, despite no chemical or mechanical pruritic input101. Mice exhibiting contagious itch behavior show higher level of c-Fos expression, as well as lower level of GRP expression in the suprachiasmatic nucleus (SCN) of the hypothalamus, indicating that GRP+ neurons in the SCN may be involved in visually contagious itch101. The receptor of GRP is also highly expressed in the SCN, and conditional knockout of Grpr or ablation of GRPR+ neurons in the SCN reduces imitative scratching behavior. These data suggest a potential role of GRP–GRPR signaling in the SCN for processing visually contagious itch in mice , as well as anxiety-like behaviors, as revealed by the open field test (OFT) and elevated plus maze (EPM) assay, suggesting a negative emotional component of itch sensation104. Moreover, the neuronal activity of amygdala-projecting brain areas such as the PBN and mPFC was also increased after pruritogen injection104. Importantly, optogenetic activation of histamine-responsive amygdala neurons reduced the duration in the open arm of the EPM and center time in the OFT, suggesting that the amygdala plays a critical role in encoding the negative emotional component of itch sensation. In addition, VTA GABAergic neurons also play a critical role in encoding the aversive component of itch sensation. The activity of VTA GABAergic neurons is immediately increased after itch-induced scratching behavior105. Silencing VTA GABAergic neurons has been found to impair itch-associated CPA105, supporting the prominent role of VTA GABAergic neurons in encoding the negative emotional aspect of itch sensation. Additionally, a recent study demonstrated that PAG is critical for modulating the affective component of itch. Pharmacogenetic activation of PAG GABAergic neurons impairs itch-associated CPA, suggesting that PAG GABAergic neurons modulate the affective component of itch107. At the spinal level, the negative emotional signal associated with itch is conveyed by GRPR+ neurons106.Recent animal studies have further explored the possible mechanism underlying the negative emotional component of itch sensation. A subpopulation of CeA neurons was activated by various types of pruritogens, and a subset of these itch-responsive neurons also responded to noxious stimuli50, which could facilitate the scratching behavior. VTA is a widely recognized reward center in the brain and is capable of encoding both value and salience97. Human functional imaging studies have indicated that VTA is involved in the pleasure associated with scratching an itch90, and this was further tested in a recent animal study105. Recording the population activity of DA neurons in the mouse VTA in response to pruritogen-induced scratching behavior with fiber photometry showed that acute pruritic stimuli activated VTA DA neurons with a several-second delay upon scratching onset. Moreover, the activation of DA neurons vanished during scratching attempts when the scratching behavior after pruritogen application was prevented with a custom-made collar, suggesting that VTA DA neurons might signal the pleasure associated with scratching an itch105. Furthermore, inactivating VTA DA neurons attenuated scratch-associated conditional place preference (CPP), further supporting the crucial role of the VTA in regulating the emotional components of the itch-scratch cycle  plays an important role in facilitating the itch-scratch cycle, since the ablation or pharmacogenetic inhibition of these neurons reduces itch-induced scratching behavior. Pharmacogenetic or optogenetic activation of PAG Tac1+ neurons has been found to directly induce a robust itch-like scratching behavior. The scratching behavior induced by activation of Tac1+ neurons is reduced by ablation of spinal GRPR+ neurons, suggesting that PAG Tac1+ neurons modulate the spinal itch processing through a descending pathway109. PAG Tac1+ neurons form glutamatergic synapses with spinal cord-projecting RVM neurons, supporting the idea that the PAGTac1-RVM circuit plays an important role in the descending regulation of itch sensation  selectively modulate histamine-dependent itch processingtch Fig. , further5. Several dedicated neural circuits have been demonstrated to process different forms of itch as well as modulate different aspects of itch sensation. Despite all these exciting advances in the itch field, several key questions remain to be addressed.During the past decade, tremendous progress has been made in deciphering the circuit mechanisms of itch sensation in both the spinal cord and the brain95. How is itch discriminated from other somatosensations? Where is the perceptual distinction between different somatosensory modalities achieved? To address these questions, it will be important to record the activity of neurons with cellular resolution along the sensory pathways during itch processing in awake animals, which can be achieved by in vivo extracellular recording or calcium imaging with endoscopy or miniaturized two-photon microscopy120.There is still debate about the coding mechanism of itch60. Whether those circuits actively involved in chemical itch modulation can also provide substantial modulation of mechanical itch or whether these two different forms of itch sensation are dynamically regulated by distinct inhibitory neuronal networks remains to be elucidated. How do different somatosensory modalities interact at the spinal level? The identity of the spinal projection neurons that send different types of itch information to the brain is still unknown. What is the functional difference between the spinothalamic and spinoparabrachial projections in conveying itch as well as other somatosensory information? Recent sequencing studies have provided more insight into the classification of neurons in the spinal cord122, and such knowledge together with new tools for dissecting local circuits will guide further dissection of spinal circuits. Furthermore, the PBN is demonstrated to be a first relay station for itch transmission in the brain26, yet the genetic identity of itch-responsive PBN neurons needs to be determined. Since PBN has been shown to participate in many other physiological processes, such as pain sensation123, it will be important to further investigate the integration or segregation of different sensory information in the PBN.Multiple key components of the neural circuit for itch signal processing have been identified at the spinal level. However, understanding of the spinal circuit for itch processing and modulation is not complete. We know more about the inhibitory neuronal control of transmission for chemical itch, but less is known about the roles of different inhibitory neuronal populations in the regulation of mechanical itchFinally, our knowledge of the modulatory network of itch sensation, especially the emotional and motivational components of itch, is still limited and requires further systematic investigation. It is also critical to gain a deep understanding of the circuit mechanism underlying the positive emotional component, which is one of the major driving forces for the vicious itch-scratch cycle. Developing new paradigms will be the key to address these questions. Additionally, the circuit mechanisms underlying chronic itch are largely unknown. Dissection of the mechanism of chronic itch will guide the development of new therapeutic approaches for itch.


Colorectal cancer and other cancers often metastasize to the liver in later stages of the disease, contributing significantly to patient death. While the biomechanical properties of the liver parenchyma  are known to affect tumor cell behavior in primary and metastatic tumors, the role of these properties in driving or inhibiting metastatic inception remains poorly understood, as are the longer-term multicellular dynamics. This study adopts a multi-model approach to study the dynamics of tumor-parenchyma biomechanical interactions during metastatic seeding and growth. We employ a detailed poroviscoelastic model of a liver lobule to study how micrometastases disrupt flow and pressure on short time scales. Results from short-time simulations in detailed single hepatic lobules motivate constitutive relations and biological hypotheses for a minimal agent-based model of metastatic growth in centimeter-scale tissue over months-long time scales. After a parameter space investigation, we find that the balance of basic tumor-parenchyma biomechanical interactions on shorter time scales  and longer time scales (plastic tissue relaxation over hours) can explain a broad range of behaviors of micrometastases, without the need for complex molecular-scale signaling. These interactions may arrest the growth of micrometastases in a dormant state and prevent newly arriving cancer cells from establishing successful metastatic foci. Moreover, the simulations indicate ways in which dormant tumors could “reawaken” after changes in parenchymal tissue mechanical properties, as may arise during aging or following acute liver illness or injury. We conclude that the proposed modeling approach yields insight into the role of tumor-parenchyma biomechanics in promoting liver metastatic growth, and advances the longer term goal of identifying conditions to clinically arrest and reverse the course of late-stage cancer. Liver cells are organized in an intricate and complex architecture. The main structural units are the hepatic lobules, within which hepatocytes are arranged in a hexagonal pattern around the central vein. The portal triads  are found at the vertices of these hepatic lobules. See Fig. 2.The liver performs critical physiological processes such as macronutrient metabolism, bile creation, and filtration of toxic substances from the blood4. The reciprocal interactions between resident cells in the liver microenvironment govern the successful formation of metastatic tumor foci. Significant research has focused on the molecular, single-cell alterations that contribute to metastatic disease, yet the biophysical interactions between tumor cells and the metastatic microenvironment remain to be elucidated at multicellular scales over long times. This knowledge gap reflects both the emphasis of reductionist biology on molecular mechanisms of individual cancer cells and the difficulty in longitudinally observing tumor-parenchyma interaction dynamics in animal models or patients. Fresh insights on these interactions could improve therapeutic interventions and patient outcomes.While the liver is critically involved in normal physiological functions, it is also a common site for metastatic disease. Liver metastases are a major barrier in cancer management. In particular, for colorectal cancer (CRC), patient mortality is principally due to the spread of disease to distant organs, with the liver being the predominant site in approximately 70% of the cases. This metastatic organotropism is mainly attributed to the drainage of the colon and rectum blood supply directly into the liver through the portal vein. Tumor cells that survive and proliferate in the liver microenvironment do so by replacing resident hepatocytes, co-opting the vasculature, adhering to hepatocyte-derived extracellular matrix (ECM), and communicating with neighboring stromal cells6. In liver tissues, prior injuries and diseases such as cirrhosis increase fibrosis, and alter tissue stiffness6. Aging may also contribute to changes in liver tissue stiffness, as well as increase the likelihood of tissue fibrosis following an acute injury or illness9. The literature on the effect of such biomechanical alterations on tumor growth in the liver is seemingly inconsistent: some report low incidence of CRC metastases in cirrhotic livers (with accompanying fibrosis)10, while others have reported that the risk of CRC liver metastases may be higher in patients with cirrhosis11. Still others report that stiff tissues can promote tumor growth and metastasis in the liver . Some of these conflicting reports may lie in the difference between invasion from a primary site at the start of the metastatic cascade  and seeding and growth at a metastatic site 13. In EMT, tissue stiffness can up-regulate cell motility and matrix remodeling15, whereas stiff tissues at a metastatic site may impair tumor growth following MET13. Indeed, metastatic “seeds” arriving in a new “soil” may rest dormant for many years: a seeded metastasis may cease proliferating at a small, sub-clinical size or fail to grow at all; unknown later events may “reawaken” long-dormant metastases to resume rapid growth15. While there has been some prior study of the molecular-level triggers for metastatic reawakening , far less is understood of the potential role of direct tumor-parenchyma mechanical interactions, or how mechanical changes following injury, illness, or aging could drive a dormant micrometastasis back to a rapidly growing state. Similarly, most investigations of age-driven changes in liver tissue have focused on immunologic and molecular-scale changes to ECM, rather than more basic mechanical properties .Tissue stiffness and other biomechanical factors are known to affect primary and metastatic tumor growthA more nuanced understanding of mechanobiological feedbacks between a seeded micrometastasis and the surrounding parenchyma could help detangle these inconsistent observations. To arrive at a more detailed understanding, interstitial fluid flow disruption and mechanical pressures imposed on the surrounding parenchyma by growing micrometastases needs further investigation, including how these can lead to parencyhma deformation and death, and how tumor-parenchyma mechanical interactions can alter the growth or arrest of micrometastases. With an improved understanding of these links, critical events most likely to drive emergence of new liver metastases in CRC patients may be identified, and thereby tailor more appropriate monitoring and early interventions for these at-risk patients. The limitations inherent in artificial in vitro environments and the confounding interactions present in in vivo models may be addressed via mathematical modeling and computational simulations, which allow systematic analysis by building, testing, and refining minimal models until they can fit prior observations. This work seeks to establish a modeling platform to investigate cell-stromal interactions and the associated tissue-scale effects within the context of the liver. In the longer term, such a platform could enable evaluating potential metastatic spread and progression based on patient-specific tumor cell and liver tissue characteristics.18, liver fibrosis20, liver toxicology22, interstitial flow and mechanics in liver tissues29. Others have modeled the role of tissue mechanics on (mostly primary) tumor growth33, including some excellent work on the role of mechanosensing in individual tumor cell proliferation35, although these did not focus specifically on primary or metastatic tumor growth in liver tissues. While there is less history in specific modeling of liver metastases, some have modeled the growth of metastases in the liver38, while others have simulated the impact of ECM remodeling and macrophages in liver metastases41. There are also good examples of simulating nanotherapy treatments44 and theranostics46 of liver metastases. Others have developed network models of metastatic seeding52, but without spatiotemporal detail and without considering the impact of tumor-host biomechanical interactions.While there have been numerous modeling advances in related areas, to the best of our knowledge, no model has been developed to address tumor-parenchyma biomechanical feedback in large tissues and the impact on tumor growth and parenchyma disruption. Please see Appendix-Mathematical modeling of liver biology and metastases, and cancer mechanobiology in the Supplementary Materials for an expanded overview of the prior work. Briefly, there is a rich history of mathematical modeling of liver biology, particularly liver tissue regeneration54) can evaluate some of these dynamics, it remains difficult to resolve tumor foci at multicellular resolution over long times. Past multiscale modeling investigations have mainly focused on discrete or smaller-scale simulations, or worked to provide insights at larger, continuum scales. However, none have investigated cell-level mechanobiologic effects over large tissues, nor have they been motivated by detailed continuum models of interstitial flow to drive efficient whole-tissue scale models over long times.In spite of previous empirical and mathematical modeling efforts, the impact of parenchymal mechanics and flow on metastatic growth in the liver remains poorly understood, as is the biology regulating tumor-parenchyma interactions near the tumor boundary. And while emerging biomimetic platforms  evaluate whether disrupted interstitial flow significantly slows tumor growth by limiting the availability of cell oxygen and nutrients, and (2) determine if mechanical pressure feedbacks on tumor cell proliferation are necessary to avoid non-physical model behaviors. Our novel combination of a detailed lobule-scale flow model, mathematical analysis, and an agent-based multicellular model allows us to determine that for metastatic growth into stiff, well-perfused tissues, any minimal model of tumor-parenchyma interactions must combine substrate- and biomechanics-driven birth and death.We perform a parameter space investigation of the minimal tissue-scale model to (3) identify the conditions under which parenchymal biomechanics results in tumor dormancy, and (4) evaluate whether changes in parenchymal biomechanics  can reawaken a dormant tumor. We find that micrometastases can arrest at subclinical sizes when elastic forces  are not relieved by death or plastic relaxation in the parencyhma. We also observe sustained metastatic growth when the parenchyma cannot tolerate significant elastic deformation. These model-driving findings give dynamical context to static clinical observations. Moreover, the model predicts that changes in parenchyma tissue mechanics can push a dormant micrometastasis into sustained growth.Interestingly, the simple mathematical model predicts these phenomena without the need for complex cell signaling, ECM remodeling, or other molecular-scale phenomena. This work showcases how simple mathematical models can be computationally scaled to larger tissues to assess complex cancer biology. Future incorporation of detailed molecular scale models could help further refine how the cell and tissue agents achieve the basic behaviors in this minimal model, while the emergent multicellular behaviors help explain how molecular- and cell-scale behaviors drive long-time behavior of metastases.This study combines two modeling approaches to investigate metastatic tumor seeding and growth in liver tissues. A high-resolution poroviscoelastic (PVE) model of interstitial flow and tissue mechanics gives detailed insights on a tumor’s impact on interstitial flow, transport, mechanical pressure, and tissue deformation in and around a tumor micrometastasis for a quasi-steady tumor on short time scales. We use an efficient, minimal lattice-free agent-based model (ABM) framework to simulate individual CRC cells and parenchyma agents in centimeter-scale liver tissues over weeks and months.Unlike typical matched models where small-scale discrete models motivate large-scale continuum models, this study uses a detailed continuum model of flow mechanics to generate hypotheses and simplifications for scalable discrete models. We use the PVE model results to build constitutive relations for growth substrate transport at centimeter scale in tissues with hundreds of lobules. We also use the PVE results to motivate a minimal set of biological rules (hypotheses) to study tumor cells and parenchymal tissue in a centimeter-scale agent-based model (ABM) of tumor metastatic growth in liver tissue. The multi-model approach is summarized in Fig. In the absence of a tumor (control case), our PVE model simulations show that the flow velocity increases in an approximately exponential pattern from the portal vein inlet to the central vein outlet, with velocity magnitudes ranging from 0 to We next seeded very small micrometastases  within non-obstructed regions of parenchyma. After dimensionless analysis of a general conservation law in these two respective regions, we found that substrate transport is largely advection-driven in intact parenchyma, and primarily diffusion-dominated in tumor regions. We built on these results to obtain an approximate analytical solution to the quasi-steady distribution of cell substrates within an unobstructed lobule, as well as to approximate advection-dominated perfusion in large tissue sections. See the Supplementary Materials for further detail on the dimensional analysis and approximate solutions.62 use a diffusing growth factor  as the main negative feedback on tumor cell proliferation by scaling cell cycling with local substrate availability. As the avascular tumor grows, it consumes and depletes the substrate, thus slowing growth in a negative feedback. Other models have focused on mechanical growth feedbacks, such as work by Drasdo et al.63 or cellular automaton models that only allow proliferation when a neighboring lattice site is unoccupied65. See the excellent review in66. While simulation frameworks such as PhysiCell are capable of simultaneously modeling substrate and mechanical growth feedbacks, most models consider one or the other. This in part reflects the goal of model parsimony, where simple models are selected over more complex models (with more degrees of freedom) when they can sufficiently fit available data and observations.Most current tumor growth simulation models, such as63. To pursue this question further, we combined the analytical approximation for advection-dominated non-tumor regions from the Section “Approximate oxygen distribution in large tissue sections” in Supplementary Materials with the BioFVM diffusion solver in PhysiCell. With the methodology as outlined in Methods, we seeded a 1-67 for more discussion of default parameter values) below which proliferation is disallowed and cells may necrose. The statistics of oxygen distribution in different tumors are shown in Fig. Because the PVE simulations and subsequent analyses showed that liver parenchyma is well-perfused in tumor-free regions, we questioned whether substrate-driven growth limits alone (as a parsimonious model) can prevent non-physical behaviors  or if mechanobiologic feedbacks are also required as in60) but without a mechanobiologic feedback. We found that pressure peaked at the tumor center and decreased towards the tumor-parenchyma interface, which agrees well with Stylianopoulos et al.’s experimental and mathematical results68. See Supplementary Fig. We tested growth of a micrometastasis into liver tissue with oxygen-based growth control  We also modified the parenchyma agents to scale death with increasing elastic displacement. In cases of rapid metastatic growth (relative to plastic relaxation), parenchyma death can reduce the mechanical pressure. See additional details in the Method section. An online interactive version of this model is available at https://nanohub.org/tools/pc4livermedium.To address this, we tested whether the addition of mechanobiologic feedbacks could prevent such non-physical behaviors while opening space for a growing micrometastasis. As in work by Drasdo and ByrneWith the addition of these mechanobiologic feedbacks, we posited that the long-term fate of a metastatic seed  would depend upon the balance between (substrate- and pressure-dependent) tumor proliferation, substrate-driven tumor necrosis, elastic resistance forces generated by displaced parenchyma, mechanics-driven parenchyma death, and plastic reorganization of the parenchyma. We performed a parameter space investigation to determine how these effects interacted to drive metastatic growth.70 and simulation32 results. In particular, the base parameter set uses We initiated a series of simulations of 90 days of growth from a single seeded metastatic cell in the center of a 0.5 mentclass2pt{minimp exceeding the maximum pressure In Fig. The final tumor size decreases with increasing }μm Fig. g,h, the }μm Fig. h,i In thFor The proliferative tumor regions expand the volume of the tumor viable rim, thus deforming the nearby parenchyma. The parenchyma strain (brightest magenta: greatest relative deformation) reaches a maximum at the tumor-parenchyma interface and steadily dissipates with distance from the tumor, similarly to the poroviscoelastic simulation results. Apoptosis (black agents) occurs nearest to the tumor-parenchyma interface wherever the parenchyma strain exceeds its maximum tolerance. See Fig. Figure The model predictions of deformed liver parenchyma can be observed in clinical samples. In Fig. In a magnified view of the sample Fig. B, we see56. Note that this is also observed in clinical tissues: Fig. In the simulation results, if the parenchyma was more tolerant of deformation  left the parameters unchanged  reduced reduced tolerance to deformation) reduced To investigate the impact of a change in parenchyma tissue biomechanics , we first simulated three tumors for 90 days until reaching dormancy as described above: . The biomechanics of centimeter-scale liver tissue are simulated by coupling a poroviscoelastic (PVE) model with an agent-based model (ABM).Building upon prior calibration and validation of the PVE model for ex vivo liver tissues, we simulated coupled flow and mechanics in micrometastases and liver parenchyma in in vivo conditions. The results for fluid flow and effect on tissue strain show that changes in tumor permeability may have little to no effect when the tumor is modeled as a pressure source , we performed a parameter space investigation of the emergent dynamics of this minimal model over a range of tissue elasticity, plasticity, and tolerance of mechanical disruption. The large-scale model exploration showed that tumor-parenchyma biomechanical interactions generate pressure inside tumors and tissue strains in the nearby parenchyma. The model predictions of deformed liver parenchyma around tumor boundaries can be observed in clinical samples of colon cancer liver metastases. Matching long-time model behaviors with prior (static) clinical observations provides a dynamical context to understand these results, while also providing a theoretical bridge between known cell-scale cancer biology and clinical observations. Future work to fit this simple mathematical model with more detailed molecular models of signaling and micro-scale ECM interactions could further link known molecular biology to intermediate dynamics and long-time clinical observations.76. Model explorations generate predictions (hypotheses) that we can presently assess qualitatively against known clinical and experimental observations77. Future experiments could be tailored to these predictions and potentially further test the predictions quantitatively. The simulation results show that micrometastases may remain dormant in highly elastic parenchyma. These results are consistent with observations using a 3D ex vivo hepatic microphysiological system, which showed that MDA-MB-231 breast cancer cells were more likely to enter dormancy in more deformable tissues compared to a stiffer polystyrene scaffold78, analogous to the simulations where parenchyma agents could tolerate greater displacement. The simulation studies found that changes in parenchyma biomechanics can trigger “reawakening” of dormant micrometastases, with metastatic growth representing an invasion along paths of mechanical least resistance. In particular, simulations explored changes in liver tissue deformability that could occur when injury or aging damages the structural integrity of extracellular matrix; prior experimental cancer biology shows that such changes could occur by degradation of collagen and elastin or decreased cross-linking79. The simulations also predicted that a decrease in the overall resiliency of hepatocytes due to injury or illness may additionally lead to liver tissue changes favoring metastatic growth; this is also plausible in light of known liver biology. For example, aging as well as fatty liver disease are known to reduce the regenerative capacity of the liver parenchyma79. Illness or injuries that compromise non-parenchymal cells  could potentially contribute to both of these scenarios79. In54, a microphysiologic model system found that the presence of intact non-parenchymal cells was associated with increases in cancer attenuation signals, decreases in pro-inflammatory signals, and reduced colonization by metastatic breast cancer cells.Overall, the modeling framework presented here provides a platform to explore the mechanobiological conditions that lead to metastatic seeding, dormancy, and growth in liver tissue74. In particular, mechanical alterations including strain-stiffening, increased cross-linking, and fiber realignment are thought to be critical in promoting cancer growth and invasiveness81. Further, ECM stiffening and remodeling is promoted by the secretion of factors from stromal components that become senescent with age82. In turn, the ECM changes affect the immune system response83. Interestingly, while aging can lead to increased local tissue stiffness and fibrosis, dysregulation of ECM constituents crosslinking can also yield loose, disorganized fragmented collagen8. These changes facilitate mechanically weak tissues, which can promote tumor invasion. Thus, aging can lead to both tissue stiffening and softening, which in their own ways facilitate tumor progression and invasiveness. These biological observations are recapitulated by the simulation results of this study. The effect of tumors growing to a steady size due to mechanical feedback , few studies have focused on purely mechanical feedbacks. Our results suggest that conditions exist for which mechanical feedback alone could explain observations of metastatic progression in the liver, which should reinforce previously studied biochemical feedbacks. Further, the discrete model formulation presented herein was able to recreate continuum-level pressure and mechanics conditions with relatively simple cell rules, compared to more complex continuum-scale formulations 66. This relative simplicity offers the promise of easier translation of experimental observations to model cellular agent hypotheses to observe macro-scale effects.The modeling framework presented here has several limitations, as only a subset of key biology is represented. Integrating additional biological details could refine the results, improve quantitative matching with targeted experiments, and potentially lead to further insights. For example, incorporating additional key cell types which are known to modulate metastatic inception and progression  would improve our simulation of cell-cell and cell-matrix interactions, similarly to recent modeling efforts that explored the role of M1 macrophage-induced cytotoxicity in well-vascularized tissues such as liverThe proposed modeling framework enables evaluation of biomechanical interactions  on metastatic tumor cell seeding and growth in liver tissue. The simulation results indicate that there exist conditions for which these interactions may arrest micrometastatic growth and prevent newly arriving cancer cells from establishing new foci. Future work will examine in more detail the role of tissue microstructure, such as sinusoids, on metastatic progression. In particular, CRC metastases create glandular structures that are not fully solid. The associated microlumens could provide some stress relief and allow tumor cells and parenchymal cells to intermix, rather than undergo more homogenous compression. An improved understanding of these as well other tumor structural characteristics when interacting with liver tissue could eventually lead to patient tumor-specific therapy to prevent and reverse metastatic seeding and progression.This section illustrates the theoretical details of the PVE model, model assumptions, computational domain and numerical implementation details. The material properties and boundary conditions as well as fluid velocity, strain, stress, and pore fluid pressure results are included in the Supplementary Materials.86, and the application of poroviscoelastic models to liver tissue has been demonstrated by89 among others. The summary given here is adapted from90. A biphasic material consists of a linear elastic solid phase and an incompressible fluid phase, where relative motion between the two phases produces rate-dependent (i.e. viscoelastic) behavior91. If the fluid phase is inviscid, biphasic theory is equivalent to poroelasticity. Poroviscoelasticity extends poroelasticity by modeling the solid phase as viscoelastic, rather than linear elastic92. Equations A detailed mathematical description of the mechanics of poroviscoelastic materials was published bys and f indicate solid and fluid phase, respectively, v is velocity. If inertial forces are negligible compared with internal frictional forces, the conservation of linear momentum can be written as:k:p is hydrostatic pressure and 93.The conservation of mass for incompressible solid and fluid phases can be written as:E is the infinitesimal strain tensor and G and K are elastic shear and bulk relaxation functions, e is deviatoric strain, and 93. The relaxation functions can be defined by a Prony series expansionR is the time-dependent modulus, n, K), the specific weight of the liquid , and d is reported as the farthest distance between model faces in the z and − z direction. Each quarter-lobule model contained from 299,258 to 677,457 quadratic tetrahedral elements (Abaqus element type C3D10MP). Simulation time was 150 s and the minimum time step was 0.15 s.Model geometry is given in Fig. 28. A poroviscoelastic material model was selected because this constitutive description of liver tissue mechanics has been shown to capture the viscoelastic stress–strain behavior of the solid matrix as well as pressure-driven flow of fluid through the matrix pores28. To reduce computational time, only one-fourth of the hexagonal lobule geometry was simulated, and symmetry boundary conditions were applied to X, Y and Z planes , with centers In67 for full details on the numerical method, implementation, and convergence testing. In60, BioFVM is used as the default biotransport solver, with the system performed accurately and stably even on systems of moving cells.The method tested second-order accurate in space, first-order accurate in time, and numerically stable in typical parameter regimes for cancer and tissue biology. The method was tested for scalability on systems of millions of off-lattice agents, millions of voxels, and dozens of diffusing substrates. Setting Dirichlet nodes: any voxel j centered at position A useful feature of BioFVM are 60, Macklin and coworkers developed PhysiCell for off-lattice agent-based simulations in multicellular systems biology, with a particular focus on cancer . Each cell is a software agent that moves under the balance of mechanical forces under the “inertialess” assumption as in earlier work by Drasdo, Höhme and colleagues99. Each cell agent with index i has a phenotype 60 for full algorithmic detail, numerical testing, and a variety of examples.In60 with two agent types: tumor cells  and parenchyma agents . For this study, we do not model the fine lobular structure (particularly hepatic cords and sinusoids), but this is a continuing topic of interest for future study.In this study, we used PhysiCell Version 1.2.160 for further details and default parameter values.We use the built-in “Ki67 Advanced” cell cycle model (with default parameters), where quiescent Ki67− cells can transition to a pre-mitotic Ki67+ state, grow, and divide into two Ki67+ daughter cells. These daughter cells grow and exit the cycle to the Ki67− state. Each cell can grow, apoptose, or necrose based upon user-defined functions. Except as described later, we use the standard, built-in cell mechanics. We also begin the built-in oxygen-dependent proliferation and necrosis: notably, the Ki67− to Ki67+ transition rate increases with oxygen availability above a minimal hypoxic threshold, and the necrotic death rate increases below the threshold. Seehttps://nanohub.org/tools/pc4livermedium.For parenchyma agents, we set cycling and apoptosis to zero to model a homeostatic tissue in the absence of tumor cells. This investigation does not focus on single-cell invasion and EMT, but rather post-MET metastatic seeding and growth; we disable cell motility accordingly. The full source code is available as open source , and an interactive, online version can be run at 96);Central veins (CVs) are randomly placed across the tissue, with mean density Any closely-spaced pairs of CVs are replaced with a single CV placed at their midpoint; andRemaining space is filled with  parenchymal agents.As needed, the lobular boundaries can be estimated by generating a Voronoi mesh from the central vein positions.This study required large 2-D (thin 3-D) sections of virtual liver tissue. Here, we describe a straightforward approach to generate a synthetic tissue dataset, based upon a Voronoi approximation of hepatic lobule packing. To generate a large liver tissue: https://github.com/MathCancer/liver-mechanobiology, see Appendix-Code availability in the Supplementary Materials) to implement this algorithm; it has the random seed hard-coded to reproduce the liver tissue in this manuscript.A MATLAB script is included in the github repository  and apply the analytical approximation as a Dirichlet node. Prior to tumor seeding, every computational voxel is a Dirichlet node, and so the initial oxygenation matches the profile shown in the Supplementary Materials. We model any voxel containing tumor cells as permanently disrupted, as suggested by the PVE modeling results. To do this, we permanently remove the Dirichlet node from that voxel and use the usual diffusion solver in BioFVM.101). As inspired by69 and introduced in60, cell agents can use their potential functions to construct the local compressive pressure p, which we normalize to 1 for a cell in a densely packed 3-D tissue (12 similarly sized neighbors in dense packing) or 0.5 in 2D (6 similarly sized neighbors in dense packing). We can therefore combine this pressure with the prior oxygen-dependent cycle entry for a mechanosensory cycle arrest:In a form of mechanosensing, cells can sense compression and down-regulate cell cycling to resist overcrowding  this restorative force an additional term in the cell’s velocity:32, if a tissue remains in a strained state, it can undergo plastic reorganization. We model this relaxation process by evolving Each agent The elasto-plastic parenchyma model allows a growing micrometastasis to strain the liver tissue, potentially to the point of local tissue death. This can free space for the micrometastasis to expand.The elasto-plastic parenchyma model imparts a resistive force on the cells in a growing micrometastasis, which can lead to compression and thus down-regulate cycling to slow the growth of the metastasis.An interactive version of this model can be run online at https://nanohub.org/tools/pc4livermedium.Lastly, we assume that parenchymal agents undergo apoptosis if their deformation (displacement) 102) every 6 hours for each run. The simulations were performed on the Big Red II supercomputer at Indiana University. Big Red II has 344 XE6 (CPU-only) compute nodes and 676 XK7 “GPU-accelerated” compute nodes, providing a total of 1020 compute nodes, 21,824 processor cores, and 43,648 GB of RAM. Each XE6 node has two AMD Opteron 16-core Abu Dhabi x86–64 CPUs and 64 GB of RAM. Each model was run on a single node using 32 threads. All jobs (270 runs) were submitted as a batch. Each simulation completed in approximately 22 h, and with a total wall time of approximately of 292 h.We saved simulation outputs  for any PhysiCell-based model, including user-friendly widgets to configure and run the model and visualize results within the notebook. In addition, the Jupyter notebooks and the accompanying simulation executable can be hosted on nanoHUB104 as a cloud-hosted, interactive version of the simulation model. This facilitates multi-disciplinary communication of the model without need for downloading, compiling, and running the code.In105 at https://nanohub.org/tools/pc4livermedium. See Supplementary Fig. The agent-based model presented herein is available as a nanoHUB appTumor tissues were received from colorectal cancer patients under Institutional Review Board (IRB) approval at the Norris Comprehensive Cancer Center of USC. All subjects gave their informed consent for inclusion before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the IRB Ethics Committee of USC . The USC Translational Pathology Core processed the formalin-fixed, paraffin embedded (FFPE) tissue biospecimens. Tissue sections were prepared and stained with hematoxylin and eosin (H&E). The slides were then imaged using the Olympus VS120 Slide Scanner.Supplementary Information 1.Supplementary Information 2.


We report the first case, to our knowledge, of atrial dissociation in a patient who underwent bicaval orthotopic heart transplantation. It was believed that atrioventricular dissociation prompting pacemaker implantation likely represented donor sinus bradycardia/arrest with intact atrioventricular conduction with surgically isolated recipient atrial activity. Historically, the biatrial technique transitioned to the bicaval technique, beginning in approximately 2004, to overcome bradycardia and tachyarrhythmia complications from the biatrial technique. The bicaval technique preserves the right atrium and the anastomosis occurs in the superior vena cava (SVC) and inferior vena cava. As a result, the dual P-wave phenomenon is not expected.4 We present the first reported case, to the best of our knowledge, of dissociated P-waves in a patient who underwent bicaval OHT and required a pacemaker.Atrial dissociation consists of two P-waves, with one P-wave failing to conduct to the ventricles and being completely independent of the underlying rhythm., he showed unexpected evidence of dissociated P-waves representing dual atrial depolarizations despite a history of receiving OHT with the bicaval technique. Only one sinus node should be present, but the possibility of dual atrial tissues existing similar to in the case of the historical biatrial technique (donor and recipient) had to be entertained. The presenting ECG with AV dissociation  could represent accelerated junctional rhythm in the setting of (1) sinus arrest of recipient atria and complete AV block of intact donor atrial P-waves or (2) sinus arrest of donor atria with accelerated junctional rhythm and surgically isolated recipient P-waves. Given the need for weaning inotropes safely, the decision was made to proceed with pacemaker implantation to treat either etiology. The atrial pacemaker lead showed small amplitude intermittent atrial signals that were slower and dissociated from the ventricular rate along with AV conduction times of more than 800 ms, which likely represented isorhythmic dissociation with underlying junctional rhythm. Atrial pacing at 90 bpm accelerated the ventricle in a 1:1 fashion with an AV conduction time of 200 ms, confirming that the donor atrium had normal AV conduction  and 2B. As the P-waves continued to be dissociated despite atrial capture and ventricular acceleration, it was concluded that the dissociated P waves represented the electrically isolated recipient atrial activity. A chest X-ray demonstrated the standard atrial lead position .A 62-year-old man who underwent bicaval OHT six years prior presented for medical attention with ST-elevation myocardial infarction requiring coronary stenting, cardiogenic shock requiring percutaneous left ventricular assist support, and atrioventricular (AV) dissociation. On review of prior electrocardiograms (ECGs) recorded immediately postoperatively following OHT . Although uncommon, there have been reported cases of right atrial tissue extending as high as 4.7 cm above the sulcus terminalis in the SVC at postmortem pathology.5 Furthermore, these known variants of atrial extensions into the SVC have inherent automaticity and have been a focus of atrial tachyarrhythmia ablation.6 This patient’s SVC may have harbored extensions of recipient atrial tissue that were inadvertently preserved during his prior bicaval OHT operation and manifested as interatrial dissociation after his heart transplant. The possibility of pulmonary vein ectopy with surgical isolation accounting for the P-wave was considered but deemed unlikely given the negative V1 morphology of the P-wave. The postoperative dual P-wave baseline posed a diagnostic challenge to determine underlying rhythm when he presented with a single P-wave with AV dissociation; however, the possibilities of AV block versus donor sinus arrest warranted proceeding with a pacemaker.We present a unique case of atrial dissociation in a patient who underwent bicaval OHT. The patient’s second P-wave was thought to be likely resulting from residual atrial tissue from the recipient heart despite the patient having undergone a bicaval technique 8 The mechanisms behind the high incidence of bradyarrhythmias varies but includes autonomic changes from surgical denervation, ischemic injury of the conduction system, and sinoatrial nodal artery abnormalities.9 This patient developed late-onset sinus bradycardia/arrest likely from a combination of ischemia and rejection in the setting of acute ST-elevation myocardial infarction and cardiogenic shock.Up to 25% of patients develop bradyarrhythmias after OHT, with a majority being sinus node dysfunction. Long-term outcomes demonstrate that the bicaval technique, as compared with the biatrial one, is less likely to require permanent pacemaker (PPM) implantation because the procedure preserves the integrity of the right atrium. In fact, the incidence of PPM implantation is as high as 9% in the biatrial group versus only 2% in the bicaval group.To the best of our knowledge, this is the first reported case of atrial dissociation in a patient who underwent bicaval OHT. It was believed that AV dissociation necessitating pacemaker implantation likely represented donor sinus bradycardia/arrest with intact AV conduction with surgically isolated recipient atrial activity.Atrial dissociation following bicaval OHT is a rare phenomenon defined by two distinct and isolated P-waves.The use of the bicaval anastomotic technique can inadvertently result in biatrial anastomosis given the anatomic variability of right atrial tissue.AV dissociation can represent sinus arrest of donor atria with continuation of recipient atrial depolarizations or complete atrioventricular block. Both of these are indications for pacemaker implantation.