JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.Oral bisphosphonates are the primary medication for osteoporosis, but concerns exist regarding potential bone‐quality changes or low‐energy fractures. This cross‐sectional study used artificial intelligence methods to analyze relationships among bisphosphonate treatment duration, a wide variety of bone‐quality parameters, and low‐energy fractures. Fourier transform infrared spectroscopy and histomorphometry quantified bone‐quality parameters in 67 osteoporotic women treated with oral bisphosphonates for 1 to 14 years. Artificial intelligence methods established two models relating bisphosphonate treatment duration to bone‐quality changes and to low‐energy clinical fractures. The model relating bisphosphonate treatment duration to bone quality demonstrated optimal performance when treatment durations of 1 to 8 years were separated from treatment durations of 9 to 14 years. This may be due to a change in relationship of bone‐quality parameters with treatment duration. This model also showed that the effects of bisphosphonate treatment duration were most highly correlated with changes in means and standard deviations of infrared spectroscopically derived mineral and matrix parameters and histomorphometric bone turnover parameters. A second model related treatment duration to bone fracture in all 22 patients who fractured while on treatment with bisphosphonates for more than 8 years. This second model showed that bisphosphonate treatment duration, not hip bone mineral density (BMD), was the most strongly correlated parameter to these low‐energy bone fractures. Application of artificial intelligence enabled analysis of large quantities of structural, cellular, mineral, and matrix bone‐quality parameters to determine relationships with long‐term oral bisphosphonate treatment and fracture. Infrared spectroscopy provides clinically relevant bone‐quality information of which bone mineral purity is among the most relevant. Nine or more years of bisphosphonate treatment was associated with abnormal bone mineral purity, matrix abnormalities, and low‐energy fractures. These data justify limiting bisphosphonate treatment duration to 8 years. © 2021 The Authors.  Bisphosphonates are the primary modality for treating postmenopausal osteoporosis; their effectiveness is due to reduction of osteoclastic activity accompanying high bone turnover. Concerns exist that bisphosphonate use may be associated with bone‐quality changes that reduce load‐bearing mechanical competence and this may partially explain reported occurrences of low‐energy fractures accompanying long‐duration bisphosphonate use. These reports dampen the enthusiasm of patients and physicians for use of bisphosphonates to treat osteoporosis.Osteoporosis is a health problem of major proportions. It affects more than 200 million people worldwide, results in more than 2 million fractures annually in the US, and hospital admissions for osteoporotic fractures exceed those of heart attacks, strokes, and breast cancer combined.2, 3 and mechanical properties. These structural and material property changes may be the manifestation of bisphosphonate‐related changes in bone cell activity and bone mineral or matrix structural or compositional changes. Support for this hypothesis is found in other studies suggesting changes in relative mineralization, crystallinity, and crystal size or collagen cross‐linking with bisphosphonate use. This is shown through changes in the distribution, but not mean values, of these parameters with varying bisphosphonate treatment duration. This may be due to method of analysis, small numbers of study patients, limited treatment duration, or small changes in bone‐quality parameters with bisphosphonate treatment duration. Furthermore, the relationship between bisphosphonate treatment duration and low‐energy bone fractures is also unclear. Thus, the objectives of this study were to: (i) evaluate structural, cellular, turnover, mineral and matrix parameters of bone, and relevant patient data and their relationship with 1 to 14 years of bisphosphonate treatment duration and (ii) to determine relationships between bone fracture and structural, cellular, turnover, mineral and matrix parameters of bone, relevant patient data, and bisphosphonate treatment duration.Evidence supporting the hypothesis of a relationship between long‐term bisphosphonate use and bone‐quality changes was provided in our prior studies of bone structure8 and 22.1 Bone mineral parameters included mean values and standard deviations of the mineral to matrix ratio, carbonate to phosphate ratio, and c‐axis mineral crystal length . The bone matrix parameter was the ratio of mature to immature collagen cross‐links (cross‐linking ratio). Histomorphometric‐based bone structure parameters were bone volume/tissue volume, trabecular separation, and trabecular thickness. Histomorphometric‐based bone formation parameters were osteoid volume/bone volume, osteoid surface/bone surface, osteoid thickness, number of osteoblasts/bone perimeter, erosion surface/bone surface, number of osteoclasts/bone perimeter, mineral apposition rate, mineralizing surface/bone surface, bone formation rate/bone surface, mineralization lag time, and activation frequency. Clinical and biochemical data were obtained from the patient's medical records available at time of biopsy and were also stored in the Kentucky Bone Registry. Design of this study conformed to the Declaration of Helsinki and was approved by the University of Kentucky Institutional Review Board.Relationships among bone mineral, matrix, structural, and dynamic properties with continuous oral bisphosphonate treatment duration were evaluated from human anterior iliac crest bone samples using histomorphometry and Fourier transform infrared spectroscopy (FTIR) with a cross‐sectional design. FTIR is an established technique for analyzing various tissues in health and disease; it is especially useful for quantifying various bone mineral and matrix parameters reflecting bone quality and fracture resistance.16 Bon2.2This was a retrospective cross‐sectional study of bone samples selected from among a population collected from the past three decades and stored in the registry. The included bone samples were from a consecutive series of adult female patients with osteoporosis previously treated with oral bisphosphonates  who presented to the University of Kentucky Metabolic Bone Disease Clinic for turnover evaluation to decide optimal therapy continuation. These patients were previously diagnosed by dual‐energy X‐ray absorptiometry (DXA) of the femoral neck or lumbar spine or by low‐energy fracture regardless of DXA score and were offered a minimally invasive anterior iliac crest bone biopsy with double tetracycline labeling for workup of turnover for choice of therapy. Bone biopsies are routinely offered by our clinic to all patients with metabolic bone disorders; approximately 85% agree to the biopsy. All study patients signed an Institutional Review Board–approved consent form permitting their results to be used for subsequent scientific studies. Low‐energy fractures were defined as radiologically observed fractures occurring in the absence of documented trauma.2.3The inclusion criteria were adult (age older than 21 years) female patients with osteoporosis who were treated with oral bisphosphonates for at least 1 year and had verified intake of double‐tetracycline labeling. The exclusion criteria were genetic diseases , chronic kidney or liver diseases, primary hyperparathyroidism, neoplasms, or previous treatment with medications known to alter bone metabolism . Patients presently or previously treated with anticonvulsants or long‐term steroids or those with documented chronic alcoholism, drug addiction, malabsorption, malignancy, bariatric surgery, Paget's disease, osteogenesis imperfecta, hemiplegia or paraplegia, organic illness with potential influence on bone metabolism, or uncontrolled systemic illness were also excluded.2.4 Serial sections of 3‐μm and 7‐μm thickness were cut with a microtome equipped with a carbide‐edged knife. Sections were stained with modified Masson‐Goldner trichrome stain. Unstained sections were prepared for phase‐contrast and fluorescence light microscopy. Bone histomorphometry for static and dynamic parameters of bone structure, formation, and resorption was performed at a magnification of 200× using the Osteoplan II system . All measured histomorphometric parameters complied with the recommendations of the nomenclature committee of the American Society for Bone and Mineral Research.Before bone biopsy, patients received oral demeclocycline hydrochloride (300 mg) twice daily for 2 days, followed by a 10‐day tetracycline‐free interval and another course of tetracycline hydrochloride (250 mg) twice daily for 4 days. Anterior iliac crest bone biopsies were performed under local anesthesia after an additional 4 days. Iliac crest bone samples were fixed with ethanol at room temperature, dehydrated, and embedded in polymethylmethacrylate as described previously.17 Ser2.5Two to five 4‐μm‐thick sections were cut from each embedded bone sample, and each section was individually sandwiched between two barium fluoride discs. The sandwiched sample was placed on the stage of a Nexus 6700 Fourier Transform Infrared Spectrometer . Infrared spectra were collected over most of the trabecular bone area of each sample using an automated routine. Separate background scans were done to enable spectral subtraction and correction of errors attributable to the environment, BaF discs, and embedding material.202.6 Mean values and standard deviations of each spectroscopic parameter within each sample were determined. Approximately 325 individual bone areas, each 6 μm × 6 μm, were scanned per patient.Analysis of these spectroscopic images began by automated identification of the area of bone scanned by the infrared beam. This area was defined as the sum of 6 μm × 6 μm scanned pixels. Location of these pixels were determined from a grid established by passing a straight line along the longitudinal axis of the trabeculum and then arranging a series of parallel lines spaced 6 μm apart along this line. These parallel lines ranged in length from one edge of a trabeculum to the opposite edge of the same trabeculum within the field of view Fig. . Values 20 MeaMeasured mineral and matrix parameters were divided into low, medium, or high categories. Thresholds for these categories were as follows: Low were those values less than the mean value of the lower half minus 1 SD, high were those greater than the mean value of the greater half plus 1 SD, and the medium category consisted of all measurements above low but below high. Specific values denoting the low or high thresholds are shown for each of the mineral and matrix parameters  was used to identify the most critical features in each candidate model based upon the data.The goal of machine‐learning techniques was to generate the best prediction of an independent variable from a variety of dependent variables. Once a satisfactory prediction was obtained, machine‐learning methods were then used to identify the parameters that contributed most to model performance. Statistical and machine‐learning analyses used these infrared spectroscopy, histomorphometry, and patient‐based parameters to understand relationships among bone quality, bisphosphonate treatment duration, and fracture. Two computational challenges were addressed. The first challenge arose from the disparity between the large number of parameters analyzed compared with the number of study subjects. This disparity frequently leads to the computationally difficult21 pro to generate a balanced set  of training data from the original data set. Cross‐validation was used to reduce random effect. Data analysis was performed using R version 3.6.3  and the R package XGBoost. Model parameters were tuned using the caret package in R.The second challenge arose from the lack of a uniform or normally distributed bisphosphonate treatment duration Fig. . Clearly26 to To support the applicability of the sample size used and the generalizability of the models developed, the effort focused on bone‐quality parameters with minimum redundancy and maximum relevance. In addition, the XGBoost model was made more conservative by restricting the maximum depth of trees, tuning the XGBoost parameters γ and η to minimize loss reduction and step size shrinkage, as well as utilizing L1 and L2 regularization techniques.33.1Pertinent characteristics of the 67 included study patients are shown Table . AnalysiThe second step of this process involved use of the identified optimal two duration categories, 1 to 8 and 9 to 14 years of bisphosphonate treatment. The bone‐quality machine‐learning model was then optimized by sequential inclusion of various infrared spectroscopic and histologic bone‐quality parameters as well as various patient parameters  variable but not as a continuous variable. This may be explained by bone quality, which changes positively with bisphosphonate use for up to 8 years of treatment, but after this period it changes negatively with continuing bisphosphonate use. This explanation is consistent with our prior finding showing increases in trabecular architecture‐based strength estimates occurring at approximately 7.5 years of bisphosphonate use, then decreases in trabecular architecture‐based strength occur with continuing use.8 It i Specifically, four of the seven largest linear coefficients in the duration model and six of the eight largest linear coefficients in the fracture model were bone‐quality parameters obtained from infrared spectroscopy. The relationship between bone quality and bisphosphonate treatment was observed not just for changes in mean values of some bone‐quality parameters but also in the distributions of these parameters. The latter was previously shown in a study demonstrating changes in heterogeneity of bone‐quality parameters with bisphosphonate treatment duration. The present study's novel application of machine‐learning methods for the holistic analysis of the large volume and variety of analyzed bone‐quality parameters confirmed prior findings and identified significant new relationships between mean values and distributions of bone‐quality parameters with bisphosphonate treatment duration.Machine‐learning analyses demonstrated the importance of bone quality and underscore previous work noting the usefulness of infrared spectroscopy for assessing bone‐quality changes.29 SpeBone mineral purity, measured using the carbonate to phosphate ratio, was the most significant bone‐quality parameter in the bisphosphonate treatment duration machine‐learning model. This ratio quantifies the relative amount of carbonate substituted for phosphate in bone mineral crystal. Increases in this ratio with increasing bisphosphonate treatment duration may be a consequence of reduced bone turnover, resulting in older bone, longer‐lived mineral, and thus more time for carbonate to substitute for phosphate in the mineral crystal structure. The importance of the carbonate to phosphate ratio in the duration and fracture machine‐learning models is underscored by publications investigating relationships among bone quality, hormone replacement therapy, as well as proximal femoral and low‐energy fractures.31 The fourth and fifth most significant parameters of the model indicate that fewer osteoclasts and reduced activation frequency accompany longer bisphosphonate treatment durations. These findings are consistent with the known effect of bisphosphonates on bone turnover and with the reported 84% reduction in remodeling activity observed in iliac crest bone samples from the previously cited study. The previously shown increase in mineral to matrix ratio with increasing bisphosphonate treatment duration was confirmed by the present study but was ranked 11th of the 15 duration‐model analyzed parameters.The second most significant parameter in the duration model was the decrease in standard deviation of bone with low collagen maturity. Thus, with increasing bisphosphonate treatment duration, the range of values for the ratio of mature to immature collagen cross‐links decreased. As expected, the duration model showed that hip BMD was a significant parameter (third in importance) directly related to bisphosphonate treatment duration. This is consistent with increased mineralization observed in a prior study of 32 postmenopausal women with osteoporosis treated with bisphosphonates for 6.4 ± 2.0 years.32 TheThe machine‐learning fracture model showed that bisphosphonate treatment duration was the most important feature predictive of the observed low‐energy fractures, ie, longer bisphosphonate treatment duration was associated with greater likelihood of fracture. This finding agrees with a study of 196,129 postmenopausal women treated with bisphosphonates showing that compared with women treated for 3 to 5 years, treatment for 8 or more years was associated with an almost fivefold increase in hazard ratio for atypical fractures.27 Differences in the direction of the relationships observed in these studies may be explained by the different patient populations.Relevance of bone mineral purity, quantified by the carbonate to phosphate ratio, was demonstrated by this parameter's rank (second most important) in the fracture machine‐learning model. This finding is supported by a study of 60 bisphosphonate‐naive female patients with fractures who had decreased carbonate to phosphate ratios in iliac crest cancellous bone compared with 60 sex‐, age‐, and BMD‐matched controls.13 DifRelative bone mineralization, quantified by the mineral‐to‐matrix quality parameter, was the third most important parameter in the machine‐learning fracture model. Thus, the likelihood of low‐energy fracture in patients treated long term with bisphosphonates is directly related to increased relative bone mineralization. This finding is consistent with data showing that increased mineralization is associated with decreased fracture toughness.35 Although hip BMD was the third most important parameter in the duration model and was directly related to bisphosphonate use, hip BMD was not among the top 25 parameters in the fracture model. This finding suggests that the low‐energy fractures in study patients treated with bisphosphonates for more than 8 years are those due to diminished bone quality and turnover changes occurring with long‐term bisphosphonate use.Bisphosphonate treatment is associated with increased hip BMD and consequentially reduction in fracture risk.36 AltAmong the many parameters studied, patient age was not a significant parameter in the treatment duration or fracture models. Spine BMD was not included among the analyzed parameters because of the inferior fracture predictive value of spine BMD compared with hip BMD.38Limitations of the study are those that are characteristic of a cross‐sectional design. In addition, most patients in the study used alendronate, thus the analyses were unable to discern the effect of other oral bisphosphonates. Inclusion of bone biopsies in the study results in a study size that is small compared with those of epidemiologic studies; however, the employed sample size is sufficient to enable machine‐learning analyses to identify key bone‐quality parameters and create models that significantly relate these parameters to treatment duration and low‐energy fracture. We recognize that 22 fractures occurring in 67 patients on long‐term bisphosphonate treatment is a relatively high percentage (32.8%); however, no selection bias can be identified to explain this result.Continuous use of oral bisphosphonates exceeding 8 years is associated with significant declines in bone mineral quality and increases in low‐energy bone fractures. These findings provide evidence for an 8‐year maximum time limit for uninterrupted treatment. Bone quality is an important aspect of skeletal health and should be considered when evaluating the efficacy of various bone metabolic therapies. Inadequate bone quality is related to increased likelihood of low‐energy fracture. Infrared spectroscopic examination of biopsied tissue samples provides critically important, but otherwise unavailable, information describing bone quality.All authors state that they have no conflicts of interest.5https://publons.com/publon/10.1002/jbm4.10549.The peer review history for this article is available at Supplemental Fig. S1. Graphical depiction of estimated linear coefficients of various bone‐quality relevant parameters as they relate to bisphosphonate treatment duration. This figure shows the full list of parameter features used in the machine‐learning model relating bisphosphonate treatment duration and bone quality, ie, “the duration model.” The magnitude of the estimated linear coefficient of each model parameter is proportional to horizontal length. Signs of these linear coefficients are depicted by position versus the centerline. Red bars extending to the left denote parameters negatively correlated to bisphosphonate treatment duration; blue bars extending to the right denote parameters positively correlated to bisphosphonate treatment.Click here for additional data file.Supplemental Fig. S2. Graphical representation showing linear coefficients of various bone‐quality relevant parameters as they relate to bone fracture in patients treated with bisphosphonates. This figure shows the full list of parameters used in the machine‐learning model relating bone fracture to bisphosphonate treatment duration and bone quality, ie, “the fracture model.” The magnitude of the linear coefficient of each model parameter is proportional to horizontal bar length. Signs of these linear coefficients are depicted by position versus the centerline. Red bars extending to the left denote parameters negatively correlated to bisphosphonate treatment duration; blue bars extending to the right denote parameters positively correlated to bisphosphonate treatment duration.Click here for additional data file.


Twin pregnancies are common and associated with pregnancy complications and adverse outcomes. Prenatal clinical management is intensive and has been hampered by inferior screening and less acceptable invasive testing. For aneuploidy screening, meta‐analyses show that non‐invasive prenatal testing (NIPT) through analysis of cell‐free DNA (cf‐DNA) is superior to serum and ultrasound‐based tests. The positive predictive value for NIPT is driven strongly by the discriminatory power of the assay and only secondarily by the prior risk. Uncertainties in a priori risks for aneuploidies in twin pregnancies are therefore of lesser importance with NIPT. Additional information on zygosity can be obtained using NIPT. Establishing zygosity can be helpful when chorionicity was not reliably established early in pregnancy or where the there is a concern for one versus two affected fetuses. In dizygotic twin pregnancies, individual fetal fractions can be measured to ensure that both values are satisfactory. Vanishing twins can be identified by NIPT. Although clinical utility of routinely detecting vanishing twins has not yet been demonstrated, there are individual cases where cf‐DNA analysis could be helpful in explaining unusual clinical or laboratory observations. We conclude that cf‐DNA analysis and ultrasound have synergistic roles in the management of multiple gestational pregnancies. What's already known about this topic?In singleton pregnancies, non‐invasive prenatal testing (NIPT) for fetal aneuploidy is more effective than conventional serum and ultrasound‐based screening tests.NIPT is more complex in dizygotic twin pregnancies due to the presence of two fetal genotypes.Overall fetal fraction is higher in twin pregnancies but the individual contribution for each fetus is lower.What does this study add?A review of cell‐free DNA testing in twin pregnancies.Individual fetal fractions in dizygotic twin pregnancies can be measured.Zygosity can be established using NIPT and this can be particularly useful when there are concerns about chorionicity or determining whether one versus two fetuses are affected.Vanishing twins can be detected through NIPT and this testing could be considered for some apparently singleton pregnancies with complications. Additional information on zygosity can also be obtained. NIPT in twin pregnancies is now endorsed by the International Society for Prenatal Diagnosis.In this paper, we review the application of this technology in multiple pregnancies. We begin by discussing the basic biology of twin pregnancies and review the data on cf‐DNA present in twin pregnancies. We then explain in detail how cf‐DNA analysis is improving clinical management of twin pregnancies.2Approximately 60%–70% of twin pregnancies are dizygotic and 20%–30% monozygotic, depending on the maternal age, use of fertility treatments, and race/ethnicity of the population. Dizygotic  twins are the product of two egg fertilizations and these pregnancies will generally have dichorionic diamniotic extra‐fetal tissues.Although all twin pregnancies are considered to be high‐risk for almost all obstetrical complications in pregnancy (except post‐date pregnancies and macrosomia), the primary associated risk factor for a poor pregnancy outcome in twin pregnancies is the chorionicity.Some studies have questioned the accuracy of ultrasound to assign chorionicity. In one large study that assumed placental pathology was accurate, the sensitivity of detecting monochorionicity was 89.8% and specificity 99.5%.3Similar to singletons, women with multiple pregnancies have cf‐DNA derived from trophoblasts in their maternal circulation . As a biomarker, fetal cf‐DNA has an inherent theoretical advantage over conventional maternal serum‐based tests because it provides a direct reflection of conceptus genotype and it can be evaluated through exquisitely powerful molecular technologies such as PCR and sequencing. Analysis of single nucleotide polymorphisms (SNPs) in cf‐DNA allows distinction between maternal and fetal sequences. In dizygotic twin pregnancies, SNPs can also allow an assessment of the cf‐DNA from each separate fetus. This can be done as a separate analytic component of a counting‐based NIPTThe fetal fraction of cfDNA in maternal plasma for women with twin pregnancies is higher, but less than two‐fold, than for women with singleton pregnancies.4From the above discussion on the ability to detect different fetal genotypes it should be clear that allele specific SNPs or other polymorphic differences between fetuses can distinguish monozygotic and di‐zygotic twin pregnancies in the late first, second and third trimesters of pregnancy. A validation study for a SNP‐based zygosity test demonstrated 100% sensitivity and specificity in a series of 95 twin pregnancies.Cases where chronicity evaluation by ultrasound was not carried out due to late gestational age at initiation of care or was otherwise not available, imaging was equivocal, or where subsequent clinical findings were suggestive of an incorrect chorionicity assignment. When dizygosity is established, the pregnancy is very likely to be dichorionic. If monozygosity is established, there is an approximately 75% chance of monochorionicity and 25% chance of dichorionicity. Zygosity testing is not a replacement for routine first trimester ultrasound evaluation of chorionicity.Cases where there was fetal anatomic abnormality or growth restriction in only one of the two fetuses and there was a concern regarding both fetuses being affected by a variably expressed or variably penetrant disorder versus one affected and one unaffected. Zygosity testing can alter the risk that one of the fetuses is unaffected and this may affect patient choice and clinical management. For example, when monozygosity has been established, restricting CVS or amniocentesis to only the anatomically abnormal fetus might be preferred.As an adjunct to other NIPT studies. For example, for pregnancies with aneuploidy positive results, monozygosity implies both are very likely to be affected versus dizygosity where, generally, only one will be affected. Similarly, in cases with suspected mosaicism, zygosity can help establish whether one or both fetuses are likely to be affected .In cases with insufficient fetal cf‐DNA concentration for an aneuploidy result. If zygosity can be established, risk can be modified prior to offering an alternative screening or diagnostic test (see below).Some examples of the clinical value of the application of zygosity testing are as follows:5In theory, since there are two separate conceptions in a dizygotic pregnancy, it would be expected that the maternal age‐specific per pregnancy rate for aneuploidy would be twice that for a singleton pregnancy and the rate for monozygotic twin pregnancies which are derived from a single egg would be similar to singletons.β HCG levels in a way that mimics Down syndrome.Conventional combined serum and ultrasound‐based screening for aneuploidy has been shown to have a lower detection rate and/or higher false‐positive rate than that achievable in singletons.The option of invasive testing is also more problematic in twin pregnancies. The risk of miscarriage has been reported to be higher in twin pregnancies compared to singleton pregnancies,Uncertainties in prior risk, limitations in distinguishing monozygotic and dizygotic twins by imaging, and decreased acceptability of follow‐up invasive testing therefore limit the efficacy of conventional approaches to aneuploidy detection in twin pregnancies. The importance of a well‐defined estimate of the prior risk becomes much less important with cf‐DNA testing where the positive predictive value is driven much more strongly by the discriminatory power of the assay and only secondarily by the prior risk.Robust estimates of sensitivity are difficult to establish because of the relative rarity of affected pregnancies. Combining two recent meta‐analyses,There are some additional caveats. Most laboratories do not offer cf‐DNA screening for sex chromosome abnormalities or limit the testing to monosomy‐X in multifetal pregnancies. Many sex chromosome abnormalities are mosaic and not confirmed after initially being detected in trophoblasts.6The presence of a demised (“vanishing”) twin is common in pregnancies conceived by in vitro fertilization with rates dependent on the number of transferred embryos.A vanishing twin can be identified through cf‐DNA analysis.Although the clinical utility of routinely detecting vanishing twins through NIPT has not yet been demonstrated, there are individual cases where cf‐DNA analysis may be potentially helpful. This includes identifying situations where a vanished twin rendered serum markers less accurate in an initial screening stepThere is conflicting data on the significance of vanishing twin and its impact on pregnancy outcomes.7Although it is impractical to demonstrate efficacy in a large prospective trial, there is substantial indirect evidence to conclude that the use of cf‐DNA testing improves the screening for fetal trisomy in twin pregnancies. NIPT can evaluate zygosity and this can be particularly useful for guidance of pregnancy management in the situation where ultrasound assignment of chorionicity is uncertain or when twin pregnancies are diagnosed later in gestation. Although monozygosity does not imply monochorionicity, dizygotic twins are almost always dichorionic. The need for proper counseling of patients and the importance of an appropriate pregnancy surveillance scheme dictates that the information be made available. The presence of two or more fetal genomes in cf‐DNA, each of which is present in different concentrations, is a challenge in NIPT. While this does lead to higher rates of uninterpretable tests, it is prudent to recognize these situations and consider alternative testing. For some women and their clinicians, identification of a vanished twin through cf‐DNA testing may be useful to adjust obstetrical care. It is therefore reasonable to conclude that NIPT will play an increasingly important role in the clinical services offered to women with multiple pregnancies.cfDNA testing complements, and does not replace, first trimester ultrasound screening in multiple gestation pregnancies. In addition to providing information about chorionicity in twin pregnancies, first trimester ultrasound identifies abnormal maternal pathology  and fetal abnormalities  which can have a serious impact on outcomes in multifetal gestations. As discussed, NIPT provides superior aneuploidy screening and zygosity information. Together, ultrasound and NIPT facilitate early diagnosis of serious adverse conditions and allows couples to plan the course of the pregnancy or consider options such as pregnancy termination or fetal reduction. Application of these technologies in twin pregnancies provides an excellent illustration of the of the synergy between imaging and laboratory testing.P. Benn is a consultant and holds stock options in Natera, Inc. He is also on an Advisory Board for Menarini Biomarkers. A. Rebarber is the President of Carnegie Imaging for Women, PLLC & President of Maternal Fetal Medicine Associates, PLLC. The opinions expressed are those of the authors and not based on input from these companies.


The idea that tumour microenvironment (TME) is organised in a spatial manner will not surprise many cancer biologists; however, systematically capturing spatial architecture of TME is still not possible until recent decade. The past five years have witnessed a boom in the research of high‐throughput spatial techniques and algorithms to delineate TME at an unprecedented level. Here, we review the technological progress of spatial omics and how advanced computation methods boost multi‐modal spatial data analysis. Then, we discussed the potential clinical translations of spatial omics research in precision oncology, and proposed a transfer of spatial ecological principles to cancer biology in spatial data interpretation. So far, spatial omics is placing us in the golden age of spatial cancer research. Further development and application of spatial omics may lead to a comprehensive decoding of the TME ecosystem and bring the current spatiotemporal molecular medical research into an entirely new paradigm. Spatial omics is transforming our understanding of the cancer ecosystem at the systemic level.The integration of spatial omics and single‐cell omics can fundamentally improve our understanding of tumourigenesis and cancer microenvironment.Generating the spatial atlas of human cancers across multiple omics and timescales will potentially pioneer the revolution of spatiotemporal molecular medicine. Solving this issue requires the ability to record the transcriptional information and spatial coordinates simultaneously. An applicable way is high‐dimensional imaging ,By contrast, the state‐of‐the‐art spatial omics has now made the whole transcriptome or even epigenome measurable.22.1The first attempt to dissert the high‐throughput spatial tissue structure can be traced to laser capture microdissection (LCM)‐based strategies2.2Another strategy to capture the spatial architecture of tissue transcripts is image‐based in situ transcriptomics technology Figure . Single‐2.3Distinct from image‐based in situ transcriptomics, spatial barcode‐based approaches allow the unbiased sequencing of RNA species at the whole transcriptome level Figure . SpatialRather than placing the spatial barcodes onto chips, a new class of quantifying spatial coordination of RNAs, named microfluidic deterministic barcoding, was recently developed in Rong Fan's lab. Using a unique barcode delivering method, deterministic barcoding in tissue for spatial omics sequencing (DBiT‐seq) does not require sophisticated steps of tissue lysis to release mRNAs but presents high resolution at ∼10 μm.2.4The surging of spatial proteomics allows the detection of dozens of proteins without losing the spatial location. Mass spectrometry‐based method is one of the highly multiplex techniques to capture the protein spatial intensity. Multiplexed ion beam imaging (MIBI), using secondary ion mass spectrometry to image labelled antibodies, is able to analyse one hundred markers of the same tissue.2.5Identifying the difference of metabolites and the spatial organization of tissues is essential to decipher intra tumour heterogeneity and understand the cancer systems profoundly, however, the spatial metabolic features of tumour s are largely unclear. Matrix‐assisted laser desorption ionization imaging mass spectrometry (MALDI‐IMS) allows the detection of metabolites without losing the spatial information.2.6Cancer is a multifactorial disorder associated with multiple genetic and environmental factors; hence jointly dissecting spatial multi‐omics profile may enable us to reconstruct the key processes of tumourigenesis. DBiT‐seq supports recording the spatially barcoded mRNA and profiling proteins of interest (a panel of 22 proteins) at the same time.in silico integrating spatial‐omics and single‐cell omics data. Spatially‐resolved Transcriptomics via Epitope Anchoring (STvEA) enables the enrichment of multiplex immunohistochemistry data with scRNA‐seqAnother strategy to simultaneously decode the spatial and cellular dynamics is to 3While the complexity of spatial omics data is what makes it powerful, it also makes them hard to interpret. Usually, these data are collected in batches and generated in large quantities. The large amount of data pose great challenges for computational biologists to digest the big data and construct computational pipelines. Here, we review recent computational methods spanning from data preprocessing, spatial variable gene selection, spatial clustering and tissue segmentation, to spatial inter‐cellular crosstalk Figure .3.1The first crucial step for spatial data downstream analysis is normalization against the sequencing depth. The variance of RNA read counts of each spot can be diverse partly due to heterogeneous cell type composition. To resolve this question, several algorithms have been designed for scRNA‐seq, and some of them are still suitable for ST data analysis such as SCTransform.3.2When data normalization is completed, a question naturally arises: are there spatially variable genes linked with the well‐organised tissue structures? For example, some genes show extremely variable spatial expression patterns , while some genes are ubiquitously expressed . Computationally, quantifying the spatial variable genes is hence expected to be fundamental to discovering the molecular basis of TME architecture.3.3Defining the clusters in a given tissue is fundamental for cell‐type identification and downstream functional annotation. In the context of scRNA‐seq, filtering the pure clusters, the population exhibiting identical functions, requires manual gene signature checking and tricky parameter adjustment. A robust method is to compute the entropy metric . For example, robust segmentation is able to increase the number of detected cells and allow the inference of the real cell states.3.6In the context of scRNA‐seq data analysis, decoding cell–cell communication is crucial for understanding how cells interact with each other and how such crosstalk networks are changed under specific disease conditions such as cancer.4Cancer can be viewed as a complex system.4.1The core problem hindering patients’ long‐term survival is cancer heterogeneity.+ CCL18+ macrophages in the metastatic sites.Another grand challenge in tumour ecology research is the spatial quantification of immune cells. Andersson et al. used the deconvolution algorithm4.2Not all cancer cells are created equal. As for cancer cells located in the boundary or core regions, they exhibit different phenotype states4.3Significant progress has been made in the field of single‐cell omics and we now know that TME is well‐structured with immune cells, cancer‐associated fibroblasts, and the extracellular matrix,4.4Evolution is the driving force behind cancer cell resistance or metastasis.4.5Initial data showed that spatial arrangements of TME quantified by mIHC perform better in predicting immunotherapy response comparable to existed methods .In the clinical practice, spatial omics technology may pave the way for precision pathology. It is now possible to link the pathological images with spatial gene expression profile by using machine learning or deep learning algorithms.55.1The cancer microenvironment is similar to an ecological system, a mixture with distinct cellular populations and species Figure . The est5.2Edge effect refers to a greater diversity of the community at the boundary of habitats.5.3The patch–corridor–matrix is an important theory for describing the spatial heterogeneity of ecological landscapes.5.4Biodiversity originally refers to the biological variety of life on the Earth.6To decode the tumour ecosystem, we need to model how individual cells work and how they interact with each other. Although high‐throughput spatial sequencing technologies coupled with the state‐of‐the‐art computational algorithms have greatly improved our understanding of tumour architecture, many pressing questions still remain to be answered, especially the profiling of the intact tissue structure. Existed experimental protocols merely enable the sequencing of tiny slide(s) of tissues. In fact, such profiling may represent the partial tissue expression profile and cannot fully capture the 3D architecture. An interesting example is the 3D transcriptomics reconstruction of the developing heart,We are now in the golden age for spatiotemporal molecular medicineThe author declares that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported.


Modern conservation science frequently relies on genetic tools to manage imperiled populations threatened by processes such as habitat fragmentation and infectious diseases. Translocation of individuals to restore genetic diversity (genetic rescue) is increasingly used to manage vulnerable populations  and its transmissible cancers as a model to examine the integration of knowledge of host‐pathogen evolutionary interactions with wildlife disease management.Multicellular organisms have a long evolutionary history with oncogenic phenomena. While some cancer‐protection adaptations are very old and phylogenetically well conserved, such as cancer‐suppression mechanisms Nunney , others Devils have been afflicted by a transmissible cancer  for at least 24 years  warrants a conservation strategy that facilitates these beneficial adaptations. However, current plans include translocating individuals sourced from captive, DFTD‐naïve populations into wild populations that have undergone generations of selection for resistance or tolerance. Increasing the number of susceptible individuals in diseased devil populations is likely to fuel the epidemic by increasing infection rates and potentially diluting or delaying selective processes. The adaptive response of the devil to DFTD involves interacting mechanisms and genes; thus, we suggest captive‐breeding programs incorporate wild‐sourced devils with genotypes that exhibit resistance or tolerance to DFTD. Future studies should identify evolutionary trade‐offs associated with resistance or tolerance to DFTD and their potential fitness effects.To acquire herd immunity, over two‐thirds of a population would need to be vaccinated, given the estimated reproductive rate of DFTD   and allowing natural selection to progress (evolutionary rescue). In the case of devils, where much empirical evidence shows rapid evolution in response to DFTD in the wild and potential functional genetic changes have been identified, we argue that management of adaptive genetic diversity should be prioritized.Captive‐breeding programs to maintain genetic diversity are fundamental to protecting vulnerable populations. The case of Tasmanian devils and their cancer epizootics illustrates the importance of developing cross‐disciplinary approaches capable of timely assessments of the ecoevolutionary processes between hosts and pathogens and their consideration for management. Therefore, we encourage managers and researchers to engage in a scientific debate that integrates new knowledge and offers novel methods to assess the effects of management on evolutionary and epidemiological dynamics in the devil‐DFTD‐DFT2 system.first, do no harm.Evolutionary responses to pathogens with high mortality are not restricted to devils and DFTD. There is evidence of rapid evolutionary change in other host populations subject to emerging wildlife diseases (e.g., frogs in Central America exposed to chytrid fungus [Voyles et al.


BMN). Here we revisit our work, seeking to identify the stages of the pipeline where the greatest variation between analysis software is induced. We carry out further analyses on the three datasets evaluated in BMN, employing a common processing strategy across parts of the analysis workflow and then utilizing procedures from three software packages  across the remaining steps of the pipeline. We use quantitative methods to compare the statistical maps and isolate the main stages of the workflow where the three packages diverge. Across all datasets, we find that variation between the packages' results is largely attributable to a handful of individual analysis stages, and that these sources of variability were heterogeneous across the datasets . We also observe areas of the analysis workflow where changing the software package causes minimal differences in the final results, finding that the group‐level results were largely unaffected by which software package was used to model the low‐frequency fMRI drifts.Task‐fMRI researchers have great flexibility as to how they analyze their data, with multiple methodological options to choose from at each stage of the analysis workflow. While the development of tools and techniques has broadened our horizons for comprehending the complexities of the human brain, a growing body of research has highlighted the pitfalls of such methodological plurality. In a recent study, we found that the choice of software package used to run the analysis pipeline can have a considerable impact on the final group‐level results of a task‐fMRI investigation (Bowring et al., 2019,  In this article, we seek to identify the stages of the pipeline where the greatest variation between analysis software is induced. We carry out further analyses on the three datasets evaluated in Bowring, Maumet, and Nichols, 2019, employing a common processing strategy across parts of the analysis workflow and then utilizing procedures from AFNI, FSL, and SPM across the remaining steps of the pipeline. Across all datasets, we find that variation between the packages' results is largely attributable to a handful of individual analysis stages, and that these sources of variability were heterogeneous across the datasets. On the contrary, the large degree of flexibility in neuroimaging analysis workflows has been pinpointed as an aspect of the field that can hinder reproducibility , we discovered that it is not just the procedures comprising the analysis pipeline that can induce variation across fMRI results, but also the choice of software package through which the analysis is conducted. We reanalyzed three datasets connected to three published task‐fMRI studies within the three most widely‐used neuroimaging software packages—AFNI , wFNI Cox, , FSL  and then interchange pipeline elements between software for the remaining stages of the analysis. By comparing all sets of our analysis results, we isolate the key stages of the workflow where the three packages diverge. Ultimately, we find that the variation between the packages' results is largely attributable to sizable processing differences at a handful of key analysis stages, and that these sources of variability can be heterogeneous across datasets. Finally, for each study we apply an image‐based meta‐analysis procedure recently used in Botvinik‐Nezer et al.  data repository . Participants took part in an antisaccade task where a visual stimulus was presented in each trial and subjects were instructed to quickly fixate their gaze on the side of the screen opposite to the stimulus. Prior to a trial, subjects were given a visual cue to signal whether or not they had the potential to win a monetary reward based on their upcoming performance . We reproduce the pipeline used to obtain the main effect of time activation map, an 2.2In BMN we reanalyzed the ds000001 and ds000109 studies described in the previous section using each of the three software packages: AFNI , we conducted a collection of similar pipelines except this time implementing For pipelines that used an identical preprocessing strategy, a common minimal preprocessing workflow was applied to each of the datasets using fMRIPrep 20.0.02 , the following preprocessing was performed. First, a reference volume and its skull‐stripped version were generated using a custom methodology of fMRIPrep. Susceptibility distortion correction (SDC) was omitted. The BOLD reference was then co‐registered to the T1w reference using bbregister (FreeSurfer) which implements boundary‐based registration , FSL (version 6.0.3), and SPM  were used. At the subject‐level, modeling was partitioned into three separate components: the fMRI signal model, noise model, and low‐frequency drift model. Specifically, the fMRI signal model concerns how each software package models the hemodynamic response function to obtain the task‐related regressors in the GLM, as well as how any parametric modulations or temporal derivatives are included in the GLM to model aspects of the BOLD response to the task conditions. The noise model pertains to how each package models temporal autocorrelation for prewhitening the fMRI data . However, exchanging the fMRI signal and low‐frequency drift models between software could be done easily, by simply interchanging the relevant regressors in the design matrix. Because of this, for each of our hybrid analysis pipelines the choice of software used for modeling the noise ultimately determined the package through which the subject‐level analyses were conducted. For example, a hybrid pipeline using FSL's first‐level noise model with AFNI's first‐level fMRI signal model and drift model would be implemented within FSL, except the regressors in the design matrix for modeling the fMRI signal and low‐frequency drifts were then interchanged with the corresponding regressors from the design matrix generated by running the complete analysis within AFNI. In addition to this, six motion parameters (translations and rotations) estimated as part of the preprocessing workflow were included in all first‐level analysis models as nuisance regressors. Finally, for each hybrid pipeline the subject‐level contrast of parameter estimate maps were inputted into the software package specified by the workflow for group‐level analysis and inference.1 (far‐left) and 7 (far‐right) for each study display the pipelines that were carried out in BMN, where a single software package was used to conduct the entire analysis workflow . Pipelines labeled 2 and 6 are similar, except that preprocessing was carried out using the fMRIPrep workflow described in the previous section. Pipelines labeled 3–5 include further manipulations, each step interchanging one aspect of the subject‐ and group‐level modeling as described above. For ds000001 and ds000109, modifications to the workflow were considered relative to the pipeline where the entire analysis was carried out within FSL  or SPM (SF pipelines), as well as interchanging the preprocessing subflow with fMRIPrep. For ds000120, where group‐level analysis in FSL was not feasible, the pipeline carried out entirely in SPM  were obtained for each pair of unthresholded group‐level statistic maps to evaluate differences in the overall activation profiles produced from each analysis workflow. As well as this, Dice coefficients were obtained for all pairwise combinations of thresholded statistic maps in order to compare the final locations of activation given by each analysis pipeline after correction for multiple comparisons. For a pair of thresholded maps, the Dice coefficient is calculated as the volume of the intersection of the two maps divided by the average of the volume's of each separate thresholded image. In other words, Dice measures the overlap of voxels between two sets of thresholded maps relative to the total spatial extent covered by both maps' activations . With each Dice coefficient, the percentage of “spill‐over” activation was also computed, that is, the percentage of activation in one pipeline's thresholded statistic map that fell outside of the analysis mask of the other pipeline.Two comparison methods were considered to assess the nature of pipeline‐variability across each studies' collection of group‐level statistical results. Correlations  to yield a “consensus” activation map, that is, the set of brain regions where significant activation was unanimous across r et al. , where i1 and 7 that were carried out as part of BMN), AFNI and FSL scripts were written in Python 3.7.6 and SPM scripts were written in GNU Octave (version 4.4.1). Scripts were made generalizable using a series of templates to extract the stimulus timings from the raw data, carry out the fMRIPrep preprocessing workflow, and subsequently conduct subject‐ and group‐level analyses. A master script for each dataset took the templates as inputs, replacing various holding variables to create distinct batch scripts for each of the unique pipelines. These batch scripts were subsequently executed within the master script to obtain all sets of group‐level results.For new analyses  caused drop‐out in sizable regions of the brain during bias‐field correction in the fMRIPrep preprocessing pipeline. This subsequently led to a highly shrunken brain mask, and failure to register this subject's functional data to the template image. For these reasons subject 4 was excluded from all further analyses, and we repeated all ds000001 analyses that were previously carried out for BMN with this subject removed. As such, all ds000001 results presented here are for 15 subjects rather than the complete sample of 16 whose data were originally shared.3.1A detailed review of the regions of activation found in the thresholded maps across all pipelines for each dataset is provided in 5SF applied SPM's first‐level signal model while pipeline 6SF applied FSL's; in Figure 5AF applied AFNI's signal model while pipeline 6AF applied FSL's. In both cases, switching to FSL's signal model led to a considerable change in the final thresholded results, evidenced by the sizable difference in the Dice coefficients obtained for these two specific workflows  were generally worse than all other comparisons of pipelines varying by a single analysis step . However, it is notable that all correlations and Dice values were greater than 0.8 here, and the overall variation between results for ds000109 was much less than that observed for ds000001.Aside from preprocessing, the single analysis step that caused the most variation in the ds000109 results was the first‐level noise model. In Figures 2AS applying AFNI's group‐level modeling and inference, and pipeline 3AS applying SPM's group‐level modeling and inference. Similar to ds000109, while the main effects were captured in the thresholded F‐statistic maps by both packages , there was more disagreement over the presence of weaker effects. In this case, pipeline 2AS (that used AFNI's group‐level inference model) determined a greater quantity of smaller clusters scattered across central regions of the brain compared to pipeline 3AS (that used SPM's group model). It is also notable that AFNI's group‐level model generally determined larger F‐statistic values in the main activated regions compared to SPM  which only differed as to whether SPM or FSL was used to model the drift. In both figures, it can be seen that the thresholded results obtained for these two pipelines . The equivalent one‐sided correction was also performed to determine voxels whose consensus statistic was significantly less than zero.Slice views of the thresholded z‐statistic image presented in Figure t‐statistic maps obtained from each individual analysis workflow, as can be seen in z‐statistic for a consensus on negative activations failed to determine any brain areas that were statistically significant after FDR correction.For ds000001, the thresholded z‐statistic image presented in Figure t‐statistic maps obtained from each individual analysis workflow, displayed in z‐statistic for a consensus on negative activations failed to determine any brain areas that were statistically significant after the voxelwise correction.For ds000109, the thresholded 45AF and 6AF in Figure 5SF and 6SF in Figure t‐statistic maps . However, for ds000109 the change of signal model had minimal impact on the final results . In contrast, the interchange of ds000109's first‐level noise model produced greater relative differences, and for ds000120, the group‐level model was found to be the largest modeling source of between‐software variability . It has been previously observed in the fMRI literature that the three software packages handle orthogonalization differently . As well as this, the larger impact of the signal model for ds000001 may be attributed to varying aspects between the three studies' analysis designs. In particular, the event‐related design used for ds000001's balloon analog risk task could have been more sensitive to differences between each package's hemodynamic response model compared to the block design used for ds000109. In addition to this, while ds000109 and ds000120 did not apply any modulated regressor orthogonalization methods, for each of the three ds000001 task events represented in the GLM  the response time regressors were orthogonalized with respect to the average activity regressor  or the collection of corresponding nonparametric results . These findings also do not affect the ds000001 consensus analysis results in Section The inference procedure carried out specifically for ds000001 may have also contributed to variation in the activation clusters identified in the thresholded Knutsson  it was fOur findings for the ds000109 dataset, where the choice of first‐level noise model had the largest impact on the results relative to any other processing step, are supported by recent research where AFNI, FSL, and SPM's autocorrelation models were empirically assessed using resting‐state and task fMRI data. In Olszowy et al., F‐statistic maps for pipelines that used AFNI's group‐level inference model (compared to SPM) in Figures Our findings for the ds000120 dataset, where the group‐level model had the largest influence on the final statistical results, may be explained by differences in how AFNI and SPM model the repeated‐measures design that was used for this study. Specifically, while SPM's “full factorial design” module assumes that the variance–covariance structure of the repeated measures is uniform across all voxels, and estimates these correlations by pooling across “similar” voxels  can be quantified in order to determine where results converge. The consensus analyses carried out as part of this work exemplify the benefits to such an approach, and notably the thresholded consensus map for ds000109 , the three studies examined here were the only datasets hosted on OpenNeuro deemed suitable for extensive multi‐software analyses at the onset of our investigation. Nevertheless, a larger sample of studies will need to be analyzed to provide a further understanding of how variation in fMRI results caused by differences in analysis software generalizes across diverse datasets and task designs. Alongside this, there are various analysis parameters that were not explored in this work, including different registration methods , different voxel sizes, further thresholding methods . Once again, the interchange of first‐level signal model led to more extensive differences in the final results than any other individual processing stop, and similar to Figure 2, this was largely due to the complete loss of positive activation in the thresholded maps that occurred when SPM's first‐level signal model (pipeline 5SF) was interchanged with FSL's first‐level signal model (pipeline 6SF). Relative to the corresponding AFNI/FSL correlations presented in Figure 2, the correlation values connected to pipeline 6SF are improved here (bottom‐left). This suggests that the overall differences in the activation profiles of the unthresholded maps for pipelines 5SF and 6SF were more subtle compared to the corresponding AFNI/FSL results, but that these differences were amplified after the FWE clusterwise correction was applied to obtain the thresholded maps.Figure S2: Comparisons of the group‐level thresholded t‐statistic maps , correlation values, and Dice coefficients obtained from reanalyses of the ds000001 dataset. Blue windows compare the two sets of results obtained from pipelines 1A and 2AF, which differed only as to whether preprocessing was carried out within AFNI or within fMRIPrep, respectively. Green windows compare pipelines 6AF and 7F, which differed only as to whether preprocessing was carried out within FSL or fMRIPrep. Qualitative and quantitative comparisons displayed here show a high degree of similarity between the two sets of results where either AFNI or fMRIPrep preprocessing was used, while greater differences can be seen for the two pipelines where FSL's preprocessing workflow was interchanged with fMRIPrep. In particular, the slice views of the thresholded t‐statistic maps for pipelines 1A and 2AF look strikingly similar , while disagreement can be seen in terms of the brain regions that were positively activated for the parametric results obtained with pipelines 6AF and 7F . Alongside this, the correlation and Dice values for the pairwise comparisons of the 1A and 2AF results  were better than the corresponding values obtained for 6AF and 7F regardless of whether parametric or nonparametric inference was performed.Figure S3: Similar to Figure S2, except this time focusing on the corresponding pipeline results for reanalyses of the ds000109 dataset. While greater similarity can be seen on‐the‐whole for the ds000109 results compared to ds000001, it is notable that there was still disagreement between pipelines 6AF and 7F in terms of the negatively activated brain regions for the parametric inference results: while the thresholded results for pipeline 6AF (that used fMRIPrep's preprocessing workflow) determined two clusters of negative activation in the inferior temporal gyrus , pipeline 7F  did not determine any negative activation. Consequently, the Dice coefficient for comparisons of these two pipelines is zero (green window in the blue negative activation Dice matrix at bottom).Figure S4: Similar to Figure 3, except this time focusing on the collection of results obtained from hybrid pipelines that implemented procedures from both SPM and FSL (rather than AFNI and FSL). Once again, setting preprocessing aside, the interchange of the first‐level noise model between pipelines 3SF and 4SF led to more extensive differences in the results than any other modeling procedure. This is highlighted in the blue windows on the off‐diagonals of the correlation and Dice matrices at the bottom of the figure, where the values for pipelines 3SF and 4SF can be seen to be lower than the corresponding values for other pairs of adjacent pipelines in most cases. Similarly to Figure 3, the thresholded t‐statistic map for pipeline 4SF (that used FSL's first‐level noise model) determined slightly more smaller activation clusters than the corresponding set of results for pipeline 3SF (that used SPM's noise model).Figure S5: Comparisons of the group‐level thresholded t‐statistic maps , correlation values, and Dice coefficients obtained from reanalyses of the ds000001 dataset, focusing on the collection of results obtained from hybrid pipelines that implemented procedures from both SPM and FSL. The two sets of results given by pipelines 4SF and 5SF are displayed, which differed only as to whether SPM's or FSL's first‐level drift model was used. Overall, the change of drift model had minimal impact on the final results; pairwise comparisons show that the unthresholded maps for pipelines 4SF and 5SF were almost perfectly correlated for both the parametric and nonparametric inference cases . The Dice values are marginally worse —close to 90%—due to slightly more negative activation determined by pipeline 5SF (that used FSL's first‐level drift model) as seen in the thresholded t‐statistic maps . Nevertheless, the correlations and Dice comparisons for pipelines 4SF and 5SF are the best of all pairs of adjacent pipelines.Figure S6: Similar to Figure S5, except this time focusing on the corresponding pipeline results for reanalyses of the ds000109 dataset. Once again, the correlations  and Dice values  for pairwise comparisons of pipelines 4SF and 5SF were some of the best of any, indicating that the interchange of drift model between SPM and FSL minimally impacted the final group‐level results. In the thresholded t‐statistic images , it can be seen that the change of drift model between the two software packages only led to slight changes in the locations of some of the smaller activation clusters.Figure S7: ds000001 AFNI/FSL pipelines (parametric results). Comparisons of the group‐level thresholded t‐statistic maps , correlation values, and Dice coefficients obtained from reanalyses of the ds000001 dataset. The collection of all parametric inference results obtained from hybrid pipelines that implemented procedures from both AFNI and FSL are presented.Figure S8: ds000001 SPM/FSL pipelines (parametric results). Comparisons of the group‐level thresholded t‐statistic maps , correlation values, and Dice coefficients obtained from reanalyses of the ds000001 dataset. The collection of all results obtained from hybrid pipelines that implemented procedures from both AFNI and FSL are presented.Figure S9: ds000001 AFNI/FSL pipelines (nonparametric results). Comparisons of the group‐level thresholded t‐statistic maps , correlation values, and Dice coefficients obtained from reanalyses of the ds000001 dataset. The collection of all nonparametric inference (permutation test) results obtained from hybrid pipelines that implemented procedures from both AFNI and FSL are presented.Figure S10: ds000001 SPM/FSL pipelines (nonparametric results). Comparisons of the group‐level thresholded t‐statistic maps , correlation values, and Dice coefficients obtained from reanalyses of the ds000001 dataset. The collection of all nonparametric inference (permutation test) results obtained from hybrid pipelines that implemented procedures from both SPM and FSL are presented.Figure S11: ds000109 AFNI/FSL pipelines (parametric results). Comparisons of the group‐level thresholded t‐statistic maps , correlation values, and Dice coefficients obtained from reanalfrom both AFNI and FSL are presented. Yses of the ds000109 dataset. The collection of all parametric inference results obtained from hybrid pipelines that implemented procedures from both AFNI and FSL are presented.Figure S12: ds000109 AFNI/FSL pipelines (nonparametric results). Comparisons of the group‐level thresholded t‐statistic maps , correlation values, and Dice coefficients obtained from reanalyses of the ds000109 dataset. The collection of all nonparametric inference (permutation test) results obtained from hybrid pipelines that implemented procedures from both AFNI and FSL are presented.Figure S13: ds000109 AFNI/FSL pipelines (nonparametric results). Comparisons of the group‐level thresholded t‐statistic maps , correlation values, and Dice coefficients obtained from reimplemented procedures from both AFNI and FSL are presented. Analyses of the ds000109 dataset. The collection of all nonparametric inference (permutation test) results obtained from hybrid pipelines that implemented procedures from both AFNI and FSL are presented.Figure S14: ds000109 SPM/FSL pipelines (nonparametric results). Comparisons of the group‐level thresholded t‐statistic maps , correlation values, and Dice coefficients obtained from reanalyses of the ds000109 dataset. The collection of all nonparametric inference (permutation test) results obtained from hybrid pipelines that implemented procedures from both SPM and FSL are presented.Figure S15: ds000120 AFNI/SPM pipelines (parametric results). Comparisons of the group‐level thresholded F‐statistic maps , correlation values, and Dice coefficients obtained from reanalfrom both AFNI and SPM are presented. Yses of the ds000120 dataset. The collection of all parametric inference results obtained from hybrid pipelines that implemented proceduresFigure S16: ds000001 AFNI/FSL pipelines (parametric results). Comparisons of the group‐level unthresholded t‐statistic maps obtained from reanalyses of the ds000001 dataset. The collection of all parametric inference results obtained from hybrid pipelines that implemented procedures from both AFNI and FSL are presented.Figure S17: ds000001 SPM/FSL pipelines (parametric results). Comparisons of the group‐level unthresholded t‐statistic maps obtained from reanalyses of the ds000001 dataset. The collection of all parametric inference results obtained from hybrid pipelines that implemented procedures from both SPM and FSL are presented.Figure S18: ds000001 AFNI/FSL pipelines (nonparametric results). Comparisons of the group‐level unthresholded t‐statistic maps obtained from reanalyses of the ds000001 dataset. The collection of all nonparametric inference (permutation test) results obtained from hybrid pipelines that implemented procedures from both AFNI and FSL are presented.Figure S19: ds000001 SPM/FSL pipelines (nonparametric results). Comparisons of the group‐level unthresholded t‐statistic maps obtained from reanalyses of the ds000001 dataset. The collection of all nonparametric inference (permutation test) results obtained from hybrid pipelines that implemented procedures from both SPM and FSL are presented.Figure S20: ds000109 AFNI/FSL pipelines (parametric results). Comparisons of the group‐level unthresholded t‐statistic maps obtained from reanalyses of the ds000109 dataset. The collection of all parametric inference results obtained from hybrid pipelines that implemented procedures from both AFNI and FSL are presented.Figure S21: ds000109 SPM/FSL pipelines (parametric results). Comparisons of the group‐level unthresholded t‐statistic maps obtained from reanalyses of the ds000109 dataset. The collection of all parametric inference results obtained from hybrid pipelines that implemented procedures from both SPM and FSL are presented.Figure S22: ds000109 AFNI/FSL Pipelines (nonparametric results). Comparisons of the group‐level unthresholded t‐statistic maps obtained from reanalyses of the ds000109 dataset. The collection of all nonparametric inference (permutation test) results obtained from hybrid pipelines that implemented procedures from both AFNI and FSL are presented.Figure S23: ds000109 SPM/FSL pipelines (nonparametric results). Comparisons of the group‐level unthresholded t‐statistic maps obtained from reanalyses of the ds000109 dataset. The collection of all nonparametric inference (permutation test) results obtained from hybrid pipelines that implemented procedures from both SPM and FSL are presented.Figure S24: ds000120 AFNI/SPM pipelines (nonparametric results). Comparisons of the group‐level unthresholded F‐statistic maps obtained from reanalyses of the ds000120 dataset. The collection of all parametric inference results obtained from hybrid pipelines that implemented procedures from both AFNI and SPM are presented.Click here for additional data file.


Extracellular matrix glycoproteins play a major role in bone mineralization and modulation of osteogenesis. Among these, the intrinsically disordered protein osteopontin (OPN) is associated with the inhibition of formation, growth and proliferation of the bone mineral hydroxyapatite (HAP). Furthermore, post‐translational modifications like phosphorylation can alter conformations and interaction properties of intrinsically disordered proteins (IDPs). Therefore, the actual interaction of OPN with a HAP surface on an atomic level and how this interaction is affected by phosphorylation is of great interest. Here, we study the interaction of full‐length OPN on the surface of suspended HAP nanoparticles by solution NMR spectroscopy. We report the binding modes of this IDP and provide evidence for the influence of hyperphosphorylation on the binding character and an explanation for the differing roles in biomineralization. Our study moreover presents an easy and suitable option to measure interaction of nanoparticles in a stable suspension with full‐length proteins. The intrinsically disordered protein osteopontin is associated with modulating the formation, growth and proliferation of the bone mineral hydroxyapatite. Employing different solution NMR spectroscopy methods, we characterize the binding modes to hydroxyapatite and provide evidence for the influence of phosphorylation of the protein on the binding character and the differing roles in biomineralization. It is a secreted and chemokine‐like extracellular glycoprotein and belongs to the SIBLING  family.Phosphorylation regulates the binding interaction of OPN with HAP and therefore also mediates HAP formation and growth.[2+ ions in solution, and further, the interactions of OPN(p) with synthesized HAP nanoparticles  by solution NMR spectroscopy. NMR spectroscopy has proven to be a powerful tool in structural biology, especially for describing structures and dynamics of IDPs. The characterization of the protein‐nanoparticle interaction depends on the exploitation of multidimensional NMR experiments for the discrimination of signals in an isotopically labelled protein sample. These kinds of experiments require a major amount of time which is why sedimentation of the nanoparticles inside of a NMR tube and thus a proper determination of the interaction becomes an issue. If the nanoparticles are not small enough to create a stable suspension in the measurement buffer,[2 and different amino acids with TiO2 nanoparticles, respectively. In this study, methylcellulose (MeCe) hydrogel was chosen as a chemically inert matrix. This viscous hydrogel exhibits large porosity, which allows water‐soluble compounds to tumble freely in solution while at the same time it prevents sedimentation, and it has proven not to interact with the proteins investigated, as shown in a study of OPN binding to integrin receptors in living cells.We investigated the interaction of OPN and OPNp (hyperphosphorylated OPN) with Ca2+ to both the unphosphorylated and phosphorylated form of OPN was investigated by titration of CaCl2 and following the corresponding chemical shift perturbations from 1H‐15N HSQC NMR spectra. Binding affinities were determined by fitting the chemical shift perturbations (CSP) of the most pronounced perturbations to an analytical function , as suggested by Williamson.2+‐binding mainly in the aspartate‐rich regime , accompanied by less pronounced perturbations in the other negatively charged regions 70–79, 130–136, 178–188 and 245–260 , 174–185 and 306–311 . Considering the high binding capacity and following the idea of Kläning et al.2+ cation, the reported stoichiometry of 8–12 Ca2+ per OPNp[2+ cations.According to the size of perturbations, the binding of OPN to Ca1H‐15N HSQC NMR , 15N−R2 relaxation NMR , STD NMR  and 1HN PRE NMR  experiments. Additionally, adsorption isotherms for OPN(p) adsorbing onto HAP nanoparticles were determined . Dissociation constants KDfor OPN and OPNp of 28(17) μM and 14(4) μM, and monolayer sorption saturation capacities Χm of 0.011(3) μmol/mg and 0.006(1) μmol/mg, respectively, were obtained by fitting the data to a Langmuir isotherm.Binding of OPN(p) to the surface of HAP nanoparticles and the differences compared to the apo‐form were investigated employing 2+ cations, both OPN and OPNp show CSPs mainly in the negatively charged regions upon the interaction with HAP surfaces  is unaffected upon binding to HAP. The 15N relaxation rates . However, no significant changes in 15N−R2 values neither for OPN nor OPNp are observed upon the interaction with HAP nanoparticles, suggesting that the protein is not greatly immobilized on the HAP surface. Considering the absolute values of the CSPs and especially the similarity of the 15N−R2 values for both OPN and OPNp upon interaction with the mineral surfaces, the binding of OPN(p) to HAP in liquid seems to be of a low‐affinity nature, as tight binding events to a nanoparticle would lead to a considerable increase in transverse relaxation rates. Like for the binding to Ca2+ cations, this is consistent with a predominantly electrostatic binding mode of OPN(p) to HAP nanoparticle surfaces.Similar to the binding to Cas Figure , top. Ags Figure , middle STD NMR methods exploit the intermolecular magnetization transfer between target and ligand spins in transiently forming complexes by saturating the target resonance selectively and transferring this saturation to the contacting ligand spins which results in a decreased line intensity of the latter. We chose an on‐resonance saturation pulse of 1 s and 1.8 kHz at −5 ppm which should cover the resonances of the surface hydroxyl groups of HAP in solution NMR spectroscopy. However no major STD effects were detectable in the presence of HAP, neither for OPN nor OPNp Figure , bottom ex and hence not interacting with the HAP surface. Likewise, the phosphorylated OPNp shows mainly enhanced PRE effects upon the interaction with HAP , however somewhat more rigid at the binding domains around the residues 96–137, ∼190, ∼220 and ∼260, which leads to the observed PRE effects. The binding regions at the N‐ (21–35) and C‐terminus (306–311) regions do not reveal a major PRE effect upon binding to HAP, suggesting that the protein termini do not undergo this slight rigidification upon binding like the other binding domains.In order to further investigate these long‐range interactions of the two protein forms upon binding to HAP surfaces, PRE NMR experiments of the cysteine mutant T185 C of both forms were performed in the apo and the HAP‐bound state Figures , top, suP Figure , bottom.KD(28 vs 14 μM) and Χm (0.011 vs 0.006 μmol/mg) for OPN and OPNp upon binding to HAP . Furthermore, the central part of the protein containing the RGD‐motif (159RGD161) neither in OPN nor in OPNp shows any chemical shift perturbations or PRE effects upon binding to HAP, hence it is still available to interact with integrin receptors.To summarize, the data suggests that OPNp – more elongated structure, broadly distributed phosphoserines – binds to HAP surfaces over the full length remaining elongated and covering the mineral surface. In contrast, the non‐phosphorylated OPN binds more locally and weaker. These findings are supported by the differences in In the past, the interaction of two other calcium phosphate proteins, statherin and amelogenin, on the surface of HAP has been investigated by solid‐state NMR spectroscopy, revealing two quite different binding modes. Statherin was found to form a tight helix in the C‐terminal region at the protein‐surface interface induced upon binding to HAP. It binds very locally, including two phosphoserines adjacent to a D and two E residues, and revealing a latter‐matching mechanism.[2+ and higher affinity to HAP.These described binding modes of OPN(p) are in accordance with their reported differing roles in biomineralization. OPNp has been shown to be a natural inhibitor of calcification and mineral formation, growth and proliferation,[in vitro.[Regarding the role of the RGD‐motif interacting with bone cells, on the one hand a decreased binding affinity of OPNp to integrin receptors in comparison to the non‐phosphorylated form has been reported.[We investigated the binding of OPN to HAP nanoparticle surfaces and the impact of phosphorylation. The application of MeCe has shown to be an easy and suitable option to keep the nanoparticles in a stable suspension over several days, to maintain a solution‐like environment for the protein and not to interact with the full‐length protein investigated. NMR studies on OPN(p) upon binding to HAP revealed different binding modes. The unphosphorylated OPN binds weakly, predominantly in the poly‐D regime (residues 85 ff.), to HAP surfaces, while its main compact state is not interacting with the HAP surface and remains unaltered. By contrast, the OPNp, which has a more elongated structure due to a reduction of long‐range correlations by the hyperphosphorylation, binds to HAP surfaces over the full length as phosphorylated residues are distributed broadly over the structure, especially in the domains at residues 96–137, ∼190, ∼220 and ∼260. Furthermore, the data suggest that the structure of OPNp remains elongated upon binding, covering the HAP surface. For both OPN and OPNp the binding to HAP surfaces is of an electronegative nature and no conformational changes can be observed upon binding. Furthermore, the central part containing the RGD‐motif does not participate in the binding event and thus is still available to interact with integrin receptors. However, the different binding modes may explain the distinct biological functions of OPN and OPNp during osteogenesis and biomineralization: the rather “covering” binding mode of OPNp may explain its function as a mineralization inhibitor through physically blocking the mineral surface from further growth[Human OPN: P10451 (Uniprot); OPNp: P50447 (BMRB); Human Fam20 C: Q8IXL6 (Uniprot).The authors declare no conflict of interest.As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re‐organized for online delivery, but are not copy‐edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.SupplementaryClick here for additional data file.


The minimal information for studies of extracellular vesicles  is a field‐consensus rigour initiative of the International Society for Extracellular Vesicles (ISEV). The last update to MISEV, MISEV2018, was informed by input from more than 400 scientists and made recommendations in the six broad topics of EV nomenclature, sample collection and pre‐processing, EV separation and concentration, characterization, functional studies, and reporting requirements/exceptions. To gather opinions on MISEV and ideas for new updates, the ISEV Board of Directors canvassed previous MISEV authors and society members. Here, we share conclusions that are relevant to the ongoing evolution of the MISEV initiative and other ISEV rigour and standardization efforts. A first publication on minimal requirements, co‐authored by the ISEV Board in 2014 and now referred to as “MISEV2014” , based in part on the pre‐MISEV2018 survey in 2016  and MISEV2014  should still be addressed carefully in any MISEV revision.5Although more than a quarter of respondents had not received relevant feedback from colleagues about MISEV2018, most had  voted for “around the same length and covering the same topics, but with updated details and references”  for more attention.9Conclusion: There is no consensus on how MISEV and R&S efforts should integrate. The R&S Subcommittee can thus proceed with an ad‐hoc approach to how topic‐specific guidelines are integrated into or related to MISEV. An update to MISEV may indeed review key findings or questions of the existing task forces.In light of the formation of the Rigor and Standardization (R&S) subcommittee in 2019 and numerous topical task forces  allows scientists to report detailed methods of EV studies and identifies opportunities for improvement of reporting. EV‐TRACK was introduced by Van Deun et al.  and has 11Almost 50% felt that the MISEV renewal should follow the same, broadly inclusive authorship procedures as MISEV2018, including all interested ISEV members and other invited experts Figure . HoweverConclusion: A near‐majority of survey respondents favored the inclusive MISEV2018 model of authorship. However, a sizeable minority preferred more stringent authorship criteria, and this preference was stronger among more experienced EV researchers.12Conclusion: the current crowdsourcing effort harvested a wealth of information about outreach targets that should be exploited in parallel to a new MISEV update.A common perception about both of the prior MISEV efforts is that more visibility and outreach are needed. By December 2021, MISEV2018 had surpassed 3400 citations according to Google Scholar. However, during the same time, almost 14,000 related scholarly articles were listed in PubMed alone with EV‐related terms . Indeed, this survey showed that even some ISEV members are not familiar with MISEV and in some cases had not previously heard of it. Five hundred sixty‐three scientists answered an optional question about whether and how they could help with outreach on current and future ISEV initiatives. Forty‐four percent were willing to “reach out personally to journal editors or associates, officials of regulatory bodies, or other organizations.” More than one third said they could “write and submit an editorial” or “mini‐MISEV” summary to a journal” in their subfield. Another third were willing to “advise ISEV on influential individuals and journals (…) who should be made aware of MISEV.” Around 28% said that they could not contribute or preferred not to be involved. One hundred thirty‐nine free‐form responses were recorded, many giving the names and contact information of editors, journals, and societies that could be approached in outreach efforts. Another idea that arose during Board discussions was to perform a detailed analysis of MISEV citations across journals and fields to identify outlets for focused follow‐up.13Amidst continuing, massive growth in the EV field, efforts to enhance interpretability and comparability of results are more important than ever. Initiatives such as MISEV must evolve efficiently and be developed and presented as opportunities to strengthen EV research, rather than as barriers to innovation or entry into the field. Responses to a November 2020 survey on MISEV and other rigor efforts have revealed broad satisfaction with the shape and content of the most recent MISEV iteration, MISEV2018. However, survey takers also requested certain new emphases and additional information. In particular, the free‐form responses to several questions in the survey will provide the coordinating authors of the next MISEV manuscript with guidance for necessary changes to the framework and addition of new details and recommendations. As a final note on the survey results, fully 70% of survey respondents indicated a willingness to evaluate specific methods or reagents as needed in support of a MISEV update or other ISEV venture. This generosity and openness of EV researchers is commended and will ensure a successful new instalment of MISEV to guide the growth of EV research not just in quantity, but in quality as well.EIB: Advisory board of Sphere Gene Therapeutics Inc.; JL: owns stock in Codiak BioSciences; ownership in Exocure biosciences Inc.; consults in the EV field through Vesiclebio AB; DCIG: Consultancy agreement with Codiak BioSciences.SUPPORTING INFORMATIONClick here for additional data file.