Additional file 1 of Disparities in spatially variable gene calling highlight the need for benchmarking spatial transcriptomics methods

Additional file 1: Fig. S1. Overview of the design of benchmarking workflow. First, publicly available datasets are obtained to be used as inputs for pre-processing in a Scanpy workflow. For each dataset the processed output is then transformed into a data object most suitable for each of the SVG analysis packages. Simulated data is directly used as inputs for SVG analysis. Finally, the results are compared across packages within each dataset. Fig S2. Distinct overlap of SVGs identified by different combinations of the six tested packages. A) FF cerebellum. B) FF lymph node. C) FFPE adenocarcinoma prostate. D) FF invasive ductal carcinoma breast tissue. E) FF left ventricle. F) FFPE prostate. G) FFPE invasive ductal carcinoma breast tissue. H) FF mouse brain coronal section. Fig S3. Comparison of the number of SVGs identified by six different packages across different datasets. FFPE tissues are visualised by a cross and FF tissues are visualised with a square. High numbers of SVGs are identified in both FF and FFPE tissues, however tissues that are more transcriptionally complex seem to have more SVGs called across the dataset. Seurat reports a consistent number of SVGs across datasets as it first identifies highly variable genes then ranks their expression by how dependent it is on spatial location [53]. Fig S4. Comparison of ranked SpatialDE q-values against gene-matched SPARK-X q-values of all genes generated from each dataset. Order of plots repeats across all datasets. A) FF cerebellum. B) FF lymph node. C) FFPE adenocarcinoma prostate. D) FF invasive ductal carcinoma breast tissue. E) FFPE prostate. F) FFPE invasive ductal carcinoma breast tissue. G) FF endometrial adenocarcinoma ovarian tissue. H) FF mouse brain coronal section. Fig S5. Comparison of ranked SpatialDE q-values against gene-matched scGCO q-values of all genes generated from each dataset. A) FF cerebellum. B) FF lymph node. C) FFPE adenocarcinoma prostate. D) FF invasive ductal carcinoma breast tissue. E) FFPE prostate. F) FFPE invasive ductal carcinoma breast tissue. G) FF endometrial adenocarcinoma ovarian tissue. H) FF left ventricle. I) FF mouse brain coronal section. Fig S6. Comparison of ranked SPARK-X q-values against gene-matched scGCO q-values of all genes generated from each dataset. A) FF cerebellum. B) FF lymph node. C) FFPE adenocarcinoma prostate. D) FF invasive ductal carcinoma breast tissue. E) FFPE prostate. F) FFPE invasive ductal carcinoma breast tissue. G) FF endometrial adenocarcinoma ovarian tissue. H) FF left ventricle. I) FF mouse brain coronal section. Fig S7. Comparison of ranked SpatialDE q-values against gene-matched Squidpy q-values of all genes generated from each dataset. A) FF cerebellum. B) FF lymph node. C) FFPE adenocarcinoma prostate. D) FF invasive ductal carcinoma breast tissue. E) FFPE prostate. F) FFPE invasive ductal carcinoma breast tissue. G) FF endometrial adenocarcinoma ovarian tissue. H) FF left ventricle. I) FF mouse brain coronal section. Fig S8. Comparison of ranked SPARK-X q-values against gene-matched Squidpy q-values of all genes generated from each dataset. A) FF cerebellum. B) FF lymph node. C) FFPE adenocarcinoma prostate. D) FF invasive ductal carcinoma breast tissue. E) FFPE prostate. F) FFPE invasive ductal carcinoma breast tissue. G) FF endometrial adenocarcinoma ovarian tissue. H) FF left ventricle. I) FF mouse brain coronal section. Fig S9. Comparison of ranked scGCO q-values against gene-matched Squidpy q-values of all genes generated from each dataset. A) FF cerebellum. B) FF lymph node. C) FFPE adenocarcinoma prostate. D) FF invasive ductal carcinoma breast tissue. E) FFPE prostate. F) FFPE invasive ductal carcinoma breast tissue. G) FF endometrial adenocarcinoma ovarian tissue. H) FF left ventricle. I) FF mouse brain coronal section. Fig S10. Gene ontology enrichment results using SVGs identified by each package as inputs across datasets. A) FF cerebellum. B) FF lymph node. C) FFPE adenocarcinoma prostate. D) FF invasive ductal carcinoma breast tissue. E) FF left ventricle. F) FFPE prostate. G) FFPE invasive ductal carcinoma breast tissue. H) FF mouse brain coronal Section. Fig S11. Spatial expression patterns of SVGs identified by all packages across the FF mouse brain coronal section dataset. A) Expression of Hap1 across the hypothalamus and amygdala, cross-referenced with the Allen Mouse Brain Reference. B) Expression of Prkcd localised to the thalamus, cross-referenced with the Allen Mouse Brain Reference. C) Expression of Itpka, with highest expression in the isocortex, hippocampal formation (HPF) and cortical subplate consistent with patterns displayed in the Allen Mouse Brain Reference. D) Expression of Eef2, a known housekeeping gene in mouse (39). Fig S12. Simulated datasets generated with SRT sim. A) Location of simulated SVGs with a hotspot pattern visualised in blue, while red area indicates expression of noise genes. B) Location of simulated SVGs in both blue and green corners, while red area indicates expression of noise genes. C) Distinct overlap of SVGs compared to the control SVG list identified by different combinations of the four tested packages. 1500 SVGs were present in this dataset. D) Distinct overlap of SVGs compared to the control SVG list identified by different combinations of the four tested packages. 750 high signal SVGs and 750 low signal SVGs were present in this dataset. Fig S13. Top SVG identified by SpatialDE in negative simulated datasets generated by A) Randomising coordinates of FF Left Ventricle dataset. B) Randomising counts and coordinates of FF left ventricle dataset. C) Randomising coordinates of FFPE prostate dataset. D) Randomising counts and coordinates of FFPE prostate dataset. Fig S14. Upset plot of results generated from running SPARK-X and SpatialDE on simulated data with known pattern of SVGs

The osteology of <i>Ferrodraco lentoni</i>, an anhanguerid pterosaur from the mid-Cretaceous of Australia

Ferrodraco lentoni, an anhanguerid from the Upper Cretaceous Winton Formation of northeast Australia, is the most complete Australian pterosaur described to date, represented by a partial cranium, incomplete cervical series and wing elements. Herein we present a comprehensive osteological description of Ferrodraco, as well as an emended diagnosis for this taxon. In addition, we compare Ferrodraco with other isolated pterosaur remains from Australian Cretaceous deposits. Subtle, yet salient, differences indicate that at least three of these specimens, all derived from the upper Albian Toolebuc Formation, are distinct from Ferrodraco. However, we are uncertain whether these specimens are attributable to Mythunga camara, Aussiedraco molnari, Thapunngaka shawi, or an as yet un-named taxon. Detailed description of the postcranial material of Ferrodraco also provides an opportunity to reassess its phylogenetic position. In one analysis, Ferrodraco and Mythunga are resolved as sister taxa within Tropeognathinae, whereas in another, Ferrodraco, Mythunga, and Tropeognathus form a polytomy within Coloborhynchinae. Either way, these slight differences notwithstanding, a close relationship between Ferrodraco and Mythunga is evident, supporting the interpretation that they form a clade. By contrast, Aussiedraco molnari is resolved as a member of Targaryendraconia, a clade with a cosmopolitan distribution. The presence of several anhanguerian taxa or lineages in the late Early and early Late Cretaceous of northeast Australia is suggestive of even greater diversity in the Australian pterosaur fauna.</p

Additional file 3 of Disparities in spatially variable gene calling highlight the need for benchmarking spatial transcriptomics methods

Additional file 3. Review histor

Additional file 2 of Disparities in spatially variable gene calling highlight the need for benchmarking spatial transcriptomics methods

Additional file 2: Table S1. Reported statistic and associated p-value from each combination of pairwise comparison of results between SpatialDE, SPARK-X, scGCO and Squidpy when reported p-values were compared using the Wilcoxon signed rank test. Table S2. Sensitivity and specificity of Squidpy, SPARK-X, SpatialDE and scGCO when used to analyse both simulated datasets generated with SRTsim. Sensitivity and specificity are reported over a range of q-value cut-offs, from 0.01-1.0. Table S3. Packages included for identification of SVGs in benchmarking study. The packages are ordered by assumptions made on distribution of gene expression. The rows highlighted in blue indicate grouped packages using graph-based methods. Table S4. Overview of publicly available 10X Visium datasets to be included in benchmarking process. FF indicates tissues that are fresh frozen and FFPE indicates tissues that are formalin-fixed paraffin-embedded. The filtered output files and imaging data from the Space Ranger v1.0.0, v1.2.0 or V1.3.0 pipeline were downloaded for each dataset

New Australian sauropods shed light on Cretaceous dinosaur palaeobiogeography

Australian dinosaurs have played a rare but controversial role in the debate surrounding the effect of Gondwanan break-up on Cretaceous dinosaur distribution. Major spatiotemporal gaps in the Gondwanan Cretaceous fossil record, coupled with taxon incompleteness, have hindered research on this effect, especially in Australia. Here we report on two new sauropod specimens from the early Late Cretaceous of Queensland, Australia, that have important implications for Cretaceous dinosaur palaeobiogeography. Savannasaurus elliottorum gen. et sp. nov. comprises one of the most complete Cretaceous sauropod skeletons ever found in Australia, whereas a new specimen of Diamantinasaurus matildae includes the first ever cranial remains of an Australian sauropod. The results of a new phylogenetic analysis, in which both Savannasaurus and Diamantinasaurus are recovered within Titanosauria, were used as the basis for a quantitative palaeobiogeographical analysis of macronarian sauropods. Titanosaurs achieved a worldwide distribution by at least 125 million years ago, suggesting that mid-Cretaceous Australian sauropods represent remnants of clades which were widespread during the Early Cretaceous. These lineages would have entered Australasia via dispersal from South America, presumably across Antarctica. High latitude sauropod dispersal might have been facilitated by Albian–Turonian warming that lifted a palaeoclimatic dispersal barrier between Antarctica and South America

A juvenile <i>Diamantinasaurus matildae</i> (Dinosauria: Titanosauria) from the Upper Cretaceous Winton Formation of Queensland, Australia, with implications for sauropod ontogeny

Although sauropod dinosaur bones are the most abundant vertebrate fossils found in the Upper Cretaceous Winton Formation of northeast Australia, only subadult and adult specimens have been described to date. Herein, we describe the first juvenile sauropod from Australia, derived from the Winton Formation (Cenomanian–lower Turonian). The preserved material belongs to a single individual and is sufficiently diagnostic to classify as a juvenile Diamantinasaurus matildae—the third specimen to be referred to the species. It also enables the identification of a new local autapomorphy for Diamantinasaurus: a distinct tuberosity on the medial surface of the scapula, posterior to the junction of the acromion and the distal blade. Nevertheless, several morphological changes are observable between the juvenile and the two adult skeletons of Diamantinasaurus matildae. These include less well-defined or entirely absent muscle attachment sites on the juvenile bones relative to the heavily scarred and rugose adult specimens. Overlapping elements between the juvenile and the two adult skeletons indicate allometric changes for Diamantinasaurus matildae throughout ontogeny, with limb bones growing at a more rapid proportional rate than other skeletal elements. Finally, we review the global record of juvenile sauropod remains, demonstrating that the growth patterns of sauropods vary greatly between taxa. Although titanosaurs display a range of isometry and allometry in the growth of individual bones, it appears that allometric growth was the primary pattern for this group.</p

Affinity Purification-Mass Spectrometry and Single Fiber Physiology/Proteomics Reveals Mechanistic Insights of C18ORF25

C18ORF25 was recently shown to be phosphorylated at S67 by AMP-activated protein kinase (AMPK) in the skeletal muscle, following acute exercise in humans. Phosphorylation was shown to improve the ex vivo skeletal muscle contractile function in mice, but our understanding of the molecular mechanisms is incomplete. Here, we profiled the interactome of C18ORF25 in mouse myotubes using affinity purification coupled to mass spectrometry. This analysis included an investigation of AMPK-dependent and S67-dependent protein/protein interactions. Several nucleocytoplasmic and contractile-associated proteins were identified, which revealed a subset of GTPases that associate with C18ORF25 in an AMPK- and S67 phosphorylation-dependent manner. We confirmed that C18ORF25 is localized to the nucleus and the contractile apparatus in the skeletal muscle. Mice lacking C18Orf25 display defects in calcium handling specifically in fast-twitch muscle fibers. To investigate these mechanisms, we developed an integrated single fiber physiology and single fiber proteomic platform. The approach enabled a detailed assessment of various steps in the excitation-contraction pathway including SR calcium handling and force generation, followed by paired single fiber proteomic analysis. This enabled us to identify >700 protein/phenotype associations and 36 fiber-type specific differences, following loss of C18Orf25. Taken together, our data provide unique insights into the function of C18ORF25 and its role in skeletal muscle physiology

Affinity Purification-Mass Spectrometry and Single Fiber Physiology/Proteomics Reveals Mechanistic Insights of C18ORF25

C18ORF25 was recently shown to be phosphorylated at S67 by AMP-activated protein kinase (AMPK) in the skeletal muscle, following acute exercise in humans. Phosphorylation was shown to improve the ex vivo skeletal muscle contractile function in mice, but our understanding of the molecular mechanisms is incomplete. Here, we profiled the interactome of C18ORF25 in mouse myotubes using affinity purification coupled to mass spectrometry. This analysis included an investigation of AMPK-dependent and S67-dependent protein/protein interactions. Several nucleocytoplasmic and contractile-associated proteins were identified, which revealed a subset of GTPases that associate with C18ORF25 in an AMPK- and S67 phosphorylation-dependent manner. We confirmed that C18ORF25 is localized to the nucleus and the contractile apparatus in the skeletal muscle. Mice lacking C18Orf25 display defects in calcium handling specifically in fast-twitch muscle fibers. To investigate these mechanisms, we developed an integrated single fiber physiology and single fiber proteomic platform. The approach enabled a detailed assessment of various steps in the excitation-contraction pathway including SR calcium handling and force generation, followed by paired single fiber proteomic analysis. This enabled us to identify >700 protein/phenotype associations and 36 fiber-type specific differences, following loss of C18Orf25. Taken together, our data provide unique insights into the function of C18ORF25 and its role in skeletal muscle physiology

New Australovenator hind limb elements pertaining to the holotype reveal the most complete neovenatorid leg

We report new skeletal elements pertaining to the same individual which represents the holotype of Australovenator wintonensis, from the 'Matilda Site' in the Winton Formation (Upper Cretaceous) of western Queensland. The discovery of these new elements means that the hind limb of Australovenator is now the most completely understood hind limb among Neovenatoridae. The new hind limb elements include: the left fibula; left metatarsal IV; left pedal phalanges I-2, II-1, III-4, IV-2, IV-3; and right pedal phalanges, II-2 and III-1. The detailed descriptions are supported with three dimensional figures. These coupled with the completeness of the hind limb will increase the utility of Australovenator in comparisons with less complete neovenatorid genera. These specimens and the previously described hind limb elements of Australovenator are compared with other theropods classified as neovenatorids (including Neovenator, Chilantaisaurus, Fukuiraptor, Orkoraptor and Megaraptor). Hind limb length proportion comparisons indicate that the smaller neovenatorids Australovenator and Fukuiraptor possess more elongate and gracile hind limb elements than the larger Neovenator and Chilantaisaurus. Greater stride lengths to body size exist in both Fukuiraptor and Australovenator with the femur discovered to be proportionally shorter the rest of the hind limb length. Additionally Australovenator is identified as possessing the most elongate metatarsus. The metatarsus morphology varies with body size. The larger neoventorids possess a metatarsus with greater width but shorter length compared to smaller forms

Additional file 1 of Lessons learnt in the first year of an Australian pediatric cardio oncology clinic

Additional file 1. Supplementary data