Direct determination of diploid genome sequences.

Determining the genome sequence of an organism is challenging, yet fundamental to understanding its biology. Over the past decade, thousands of human genomes have been sequenced, contributing deeply to biomedical research. In the vast majority of cases, these have been analyzed by aligning sequence reads to a single reference genome, biasing the resulting analyses, and in general, failing to capture sequences novel to a given genome. Some de novo assemblies have been constructed free of reference bias, but nearly all were constructed by merging homologous loci into single "consensus" sequences, generally absent from nature. These assemblies do not correctly represent the diploid biology of an individual. In exactly two cases, true diploid de novo assemblies have been made, at great expense. One was generated using Sanger sequencing, and one using thousands of clone pools. Here, we demonstrate a straightforward and low-cost method for creating true diploid de novo assemblies. We make a single library from ∼1 ng of high molecular weight DNA, using the 10x Genomics microfluidic platform to partition the genome. We applied this technique to seven human samples, generating low-cost HiSeq X data, then assembled these using a new "pushbutton" algorithm, Supernova. Each computation took 2 d on a single server. Each yielded contigs longer than 100 kb, phase blocks longer than 2.5 Mb, and scaffolds longer than 15 Mb. Our method provides a scalable capability for determining the actual diploid genome sequence in a sample, opening the door to new approaches in genomic biology and medicine.

Evaluation of DISCOVAR de novo using a mosquito sample for cost-effective short-read genome assembly.

BACKGROUND: De novo reference assemblies that are affordable, practical to produce, and of sufficient quality for most downstream applications, remain an unattained goal for many taxa. Insects, which may yield too little DNA from individual specimens for long-read sequencing library construction and often have highly heterozygous genomes, can be particularly hard to assemble using inexpensive short-read sequencing data. The large number of insect species with medical or economic importance makes this a critical problem to address. RESULTS: Using the assembler DISCOVAR de novo, we assembled the genome of the African malaria mosquito Anopheles arabiensis using 250 bp reads from a single library. The resulting assembly had a contig N50 of 22,433 bp, and recovered the gene set nearly as well as the ALLPATHS-LG AaraD1 An. arabiensis assembly produced with reads from three sequencing libraries and much greater resources. DISCOVAR de novo appeared to perform better than ALLPATHS-LG in regions of low complexity. CONCLUSIONS: DISCOVAR de novo performed well assembling the genome of an insect of medical importance, using simpler sequencing input than previous anopheline assemblies. We have shown that this program is a viable tool for cost-effective assembly of a modestly-sized insect genome.

School-age effects of the newborn individualized developmental care and assessment program for preterm infants with intrauterine growth restriction: preliminary findings.

BACKGROUND: The experience in the newborn intensive care nursery results in premature infants' neurobehavioral and neurophysiological dysfunction and poorer brain structure. Preterms with severe intrauterine growth restriction are doubly jeopardized given their compromised brains. The Newborn Individualized Developmental Care and Assessment Program improved outcome at early school-age for preterms with appropriate intrauterine growth. It also showed effectiveness to nine months for preterms with intrauterine growth restriction. The current study tested effectiveness into school-age for preterms with intrauterine growth restriction regarding executive function (EF), electrophysiology (EEG) and neurostructure (MRI). METHODS: Twenty-three 9-year-old former growth-restricted preterms, randomized at birth to standard care (14 controls) or to the Newborn Individualized Developmental Care and Assessment Program (9 experimentals) were assessed with standardized measures of cognition, achievement, executive function, electroencephalography, and magnetic resonance imaging. The participating children were comparable to those lost to follow-up, and the controls to the experimentals, in terms of newborn background health and demographics. All outcome measures were corrected for mother's intelligence. Analysis techniques included two-group analysis of variance and stepwise discriminate analysis for the outcome measures, Wilks' lambda and jackknifed classification to ascertain two-group classification success per and across domains; canonical correlation analysis to explore relationships among neuropsychological, electrophysiological and neurostructural domains at school-age, and from the newborn period to school-age. RESULTS: Controls and experimentals were comparable in age at testing, anthropometric and health parameters, and in cognitive and achievement scores. Experimentals scored better in executive function, spectral coherence, and cerebellar volumes. Furthermore, executive function, spectral coherence and brain structural measures discriminated controls from experimentals. Executive function correlated with coherence and brain structure measures, and with newborn-period neurobehavioral assessment. CONCLUSION: The intervention in the intensive care nursery improved executive function as well as spectral coherence between occipital and frontal as well as parietal regions. The experimentals' cerebella were significantly larger than the controls'. These results, while preliminary, point to the possibility of long-term brain improvement even of intrauterine growth compromised preterms if individualized intervention begins with admission to the NICU and extends throughout transition home. Larger sample replications are required in order to confirm these results. CLINICAL TRIAL REGISTRATION: The study is registered as a clinical trial. The trial registration number is NCT00914108.

A spatially resolved brain region- and cell type-specific isoform atlas of the postnatal mouse brain.

Splicing varies across brain regions, but the single-cell resolution of regional variation is unclear. We present a single-cell investigation of differential isoform expression (DIE) between brain regions using single-cell long-read sequencing in mouse hippocampus and prefrontal cortex in 45 cell types at postnatal day 7 ( www.isoformAtlas.com ). Isoform tests for DIE show better performance than exon tests. We detect hundreds of DIE events traceable to cell types, often corresponding to functionally distinct protein isoforms. Mostly, one cell type is responsible for brain-region specific DIE. However, for fewer genes, multiple cell types influence DIE. Thus, regional identity can, although rarely, override cell-type specificity. Cell types indigenous to one anatomic structure display distinctive DIE, e.g. the choroid plexus epithelium manifests distinct transcription-start-site usage. Spatial transcriptomics and long-read sequencing yield a spatially resolved splicing map. Our methods quantify isoform expression with cell-type and spatial resolution and it contributes to further our understanding of how the brain integrates molecular and cellular complexity.

A single-cell and spatially resolved atlas of human breast cancers.

Breast cancers are complex cellular ecosystems where heterotypic interactions play central roles in disease progression and response to therapy. However, our knowledge of their cellular composition and organization is limited. Here we present a single-cell and spatially resolved transcriptomics analysis of human breast cancers. We developed a single-cell method of intrinsic subtype classification (SCSubtype) to reveal recurrent neoplastic cell heterogeneity. Immunophenotyping using cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) provides high-resolution immune profiles, including new PD-L1/PD-L2+ macrophage populations associated with clinical outcome. Mesenchymal cells displayed diverse functions and cell-surface protein expression through differentiation within three major lineages. Stromal-immune niches were spatially organized in tumors, offering insights into antitumor immune regulation. Using single-cell signatures, we deconvoluted large breast cancer cohorts to stratify them into nine clusters, termed 'ecotypes', with unique cellular compositions and clinical outcomes. This study provides a comprehensive transcriptional atlas of the cellular architecture of breast cancer.

Automatic segmentation of newborn brain MRI.

Quantitative brain tissue segmentation from newborn MRI offers the possibility of improved clinical decision making and diagnosis, new insight into the mechanisms of disease, and new methods for the evaluation of treatment protocols for preterm newborns. Such segmentation is challenging, however, due to the imaging characteristics of the developing brain. Existing techniques for newborn segmentation either achieve automation by ignoring critical distinctions between different tissue types or require extensive expert interaction. Because manual interaction is time consuming and introduces both bias and variability, we have developed a novel automatic segmentation algorithm for brain MRI of newborn infants. The key algorithmic contribution of this work is a new approach for automatically learning patient-specific class-conditional probability density functions. The algorithm achieves performance comparable to expert segmentations while automatically identifying cortical gray matter, subcortical gray matter, cerebrospinal fluid, myelinated white matter and unmyelinated white matter. We compared the performance of our algorithm with a previously published semi-automated algorithm and with expert-drawn images. Our algorithm achieved an accuracy comparable with methods that require undesirable manual interaction.

Comprehensive variation discovery in single human genomes.

Complete knowledge of the genetic variation in individual human genomes is a crucial foundation for understanding the etiology of disease. Genetic variation is typically characterized by sequencing individual genomes and comparing reads to a reference. Existing methods do an excellent job of detecting variants in approximately 90% of the human genome; however, calling variants in the remaining 10% of the genome (largely low-complexity sequence and segmental duplications) is challenging. To improve variant calling, we developed a new algorithm, DISCOVAR, and examined its performance on improved, low-cost sequence data. Using a newly created reference set of variants from the finished sequence of 103 randomly chosen fosmids, we find that some standard variant call sets miss up to 25% of variants. We show that the combination of new methods and improved data increases sensitivity by several fold, with the greatest impact in challenging regions of the human genome.

A magnetic resonance imaging study of cerebellar volume in tuberous sclerosis complex.

The cerebellum plays an important role in motor learning and cognition, and structural cerebellar abnormalities have been associated with cognitive impairment. In tuberous sclerosis complex, neurologic outcome is highly variable, and no consistent imaging or pathologic determinant of cognition has been firmly established. The cerebellum calls for specific attention because mouse models of tuberous sclerosis complex have demonstrated a loss of cerebellar Purkinje cells, and cases of human histologic data have demonstrated a similar loss in patients. We hypothesized that there might be a common cerebellar finding in tuberous sclerosis complex that could be measured as morphometric changes with magnetic resonance imaging. Using a robust, automated image analysis procedure, we studied 36 patients with tuberous sclerosis complex and age-matched control subjects and observed significant volume loss among patients in the cerebellar cortices and vermis. Furthermore, this effect was strongest in a subgroup of 19 patients with a known, pathogenic mutation of the tuberous sclerosis 2 gene and impacted all cerebellar structures. We conclude that patients with tuberous sclerosis complex exhibit volume loss in the cerebellum, and this loss is larger and more widespread in patients with a tuberous sclerosis 2 mutation.

School Age Effects of the Newborn Individualized Developmental Care and Assessment Program for Medically Low-Risk Preterm Infants: Preliminary Findings.

BACKGROUND: By school age, even low risk moderately preterm-born children show more neuro-cognitive deficits, underachievement, behavioral problems, and poor social adaptation than full-term peers. AIM: To evaluate the outcomes at school-age for moderately preterm-born children (29-33 weeks gestational age), appropriate in growth for gestational age (AGA) and medically at low-risk, randomized to Newborn Individualized Developmental Care and Assessment Program (NIDCAP) or standard care in the Newborn Intensive Care Unit. At school-age, the experimental (E) group will show better neuropsychological and neuro-electrophysiological function, as well as improved brain structure than the control (C) group. MATERIALS AND METHODS: The original sample consisted of 30 moderately preterm-born infants (29 to 33 weeks), 23 (8C and 15E) of them were evaluated at 8 years of age, corrected-for-prematurity with neuropsychological, EEG spectral coherence, and diffusion tensor magnetic resonance imaging (DT MRI) measures. RESULTS: E-performed significantly better than C-group children on the Kaufman Assessment Battery for Children-Second Edition (KABC-II) and trended towards better scores on the Rey-Osterrieth Complex Figure Test. They also showed more mature frontal and parietal brain connectivities, and more mature fiber tracts involving the internal capsule and the cingulum. Neurobehavioral results in the newborn period successfully predicted neuropsychological functioning at 8 years corrected age. CONCLUSION: Moderately preterm infants cared for with the NIDCAP intervention showed improved neuropsychological and neuro-electrophysiological function as well as improved brain structure at school-age.

Learning likelihoods for labeling (L3): a general multi-classifier segmentation algorithm.

PURPOSE: To develop an MRI segmentation method for brain tissues, regions, and substructures that yields improved classification accuracy. Current brain segmentation strategies include two complementary strategies. Multi-spectral classification techniques generate excellent segmentations for tissues with clear intensity contrast, but fail to identify structures defined largely by location, such as lobar parcellations and certain subcortical structures. Conversely, multi-template label fusion methods are excellent for structures defined largely by location, but perform poorly when segmenting structures that cannot be accurately identified through a consensus of registered templates. METHODS: We propose here a novel multi-classifier fusion algorithm with the advantages of both types of segmentation strategy. We illustrate and validate this algorithm using a group of 14 expertly hand-labeled images. RESULTS: Our method generated segmentations of cortical and subcortical structures that were more similar to hand-drawn segmentations than majority vote label fusion or a recently published intensity/label fusion method. CONCLUSIONS: We have presented a novel, general segmentation algorithm with the advantages of both statistical classifiers and label fusion techniques.

Highly accurate segmentation of brain tissue and subcortical gray matter from newborn MRI.

The segmentation of newborn brain MRI is important for assessing and directing treatment options for premature infants at risk for developmental disorders, abnormalities, or even death. Segmentation of infant brain MRI is particularly challenging when compared with the segmentation of images acquired from older children and adults. We sought to develop a fully automated segmentation strategy and present here a Bayesian approach utilizing an atlas of priors derived from previous segmentations and a new scheme for automatically selecting and iteratively refining classifier training data using the STAPLE algorithm. Results have been validated by comparison to hand-drawn segmentations.