An automated approach to prepare tissue-derived spatially barcoded RNA-sequencing libraries

Abstract Sequencing the nucleic acid content of individual cells or specific biological samples is becoming increasingly common. This drives the need for robust, scalable and automated library preparation protocols. Furthermore, an increased understanding of tissue heterogeneity has lead to the development of several unique sequencing protocols that aim to retain or infer spatial context. In this study, a protocol for retaining spatial information of transcripts has been adapted to run on a robotic workstation. The method spatial transcriptomics is evaluated in terms of robustness and variability through the preparation of reference RNA, as well as through preparation and sequencing of six replicate sections of a gingival tissue biopsy from a patient with periodontitis. The results are reduced technical variability between replicates and a higher throughput, processing four times more samples with less than a third of the hands on time, compared to the standard protocol.

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Optimization of Illumina® Nextera® XT Library Preparation for Analysis of Complete Mitochondrial Genomes from Human Reference Samples

10.21203/rs.3.rs-1004103/v1 ◽

2021 ◽

Author(s):

Viktorija Sukser ◽

Ivana Račić ◽

Sara Rožić ◽

Lucija Barbarić ◽

Marina Korolija

Keyword(s):

Massively Parallel Sequencing ◽

Massively Parallel ◽

Mitochondrial Genomes ◽

High Fidelity ◽

Library Preparation ◽

Equal Distribution ◽

Hands On ◽

Negative Controls ◽

Reference Samples ◽

Complete Mitochondrial Genomes

Abstract Background: Optimized and efficient library preparation workflow is one of the most important prerequisites for obtaining high quality and quantity of results in massively parallel sequencing (MPS). Our aim was to assess and optimize different steps of Illumina® Nextera® XT assay for analysis of whole mitochondrial genomes.Methods and Results: Among the three long-range high-fidelity DNA polymerases tested here, PrimeSTAR® GXL performed best in aspects of specificity and yield for mitochondrial DNA (mtDNA) enrichment. Furthermore, library quantification combined with individual library-by-library dilution outperformed bead-based normalization in terms of more equal distribution of reads per library, reduced hands-on time and simplified workflow. Increasing the number of amplification cycles in the index-adapters-adding PCR step had no adverse effect on the level of sequencing noise, which remained low both in negative controls and in samples.Conclusions: Optimizations described herein provide beneficial insights for laboratories aiming at implementation and/or advancement of similar MPS workflows (e.g. small genomes, PCR amplicons and plasmids).

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Memory for events and their spatial context: models and experiments

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.2001.0948 ◽

2001 ◽

Vol 356 (1413) ◽

pp. 1493-1503 ◽

Cited By ~ 224

Author(s):

Neil Burgess ◽

Suzanna Becker ◽

John A. King ◽

John O'Keefe

Keyword(s):

Episodic Memory ◽

Spatial Information ◽

Functional Neuroimaging ◽

Real Life ◽

Neural Representation ◽

Spatial Context ◽

Long Term Memory ◽

Long Term Storage ◽

General Aspects

The computational role of the hippocampus in memory has been characterized as: (i) an index to disparate neocortical storage sites; (ii) a time–limited store supporting neocortical long–term memory; and (iii) a content–addressable associative memory. These ideas are reviewed and related to several general aspects of episodic memory, including the differences between episodic, recognition and semantic memory, and whether hippocampal lesions differentially affect recent or remote memories. Some outstanding questions remain, such as: what characterizes episodic retrieval as opposed to other forms of read–out from memory; what triggers the storage of an event memory; and what are the neural mechanisms involved? To address these questions a neural–level model of the medial temporal and parietal roles in retrieval of the spatial context of an event is presented. This model combines the idea that retrieval of the rich context of real–life events is a central characteristic of episodic memory, and the idea that medial temporal allocentric representations are used in long–term storage while parietal egocentric representations are used to imagine, manipulate and re–experience the products of retrieval. The model is consistent with the known neural representation of spatial information in the brain, and provides an explanation for the involvement of Papez's circuit in both the representation of heading direction and in the recollection of episodic information. Two experiments relating to the model are briefly described. A functional neuroimaging study of memory for the spatial context of life–like events in virtual reality provides support for the model's functional localization. A neuropsychological experiment suggests that the hippocampus does store an allocentric representation of spatial locations.

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Deciphering spatial domains from spatially resolved transcriptomics with adaptive graph attention auto-encoder

10.1101/2021.08.21.457240 ◽

2021 ◽

Author(s):

Kangning Dong ◽

Shihua Zhang

Keyword(s):

Gene Expression ◽

Spatial Information ◽

Expression Profiles ◽

Expression Patterns ◽

Gene Expression Profiles ◽

Identification Accuracy ◽

Spatial Context ◽

Gene Expressions ◽

Spatially Resolved ◽

Spatial Domains

Recent advances in spatially resolved transcriptomics have enabled comprehensive measurements of gene expression patterns while retaining spatial context of tissue microenvironment. Deciphering the spatial context of spots in a tissue needs to use their spatial information carefully. To this end, we developed a graph attention auto- encoder framework STGATE to accurately identify spatial domains by learning low-dimensional latent embeddings via integrating spatial information and gene expression profiles. To better characterize the spatial similarity at the boundary of spatial domains, STGATE adopts an attention mechanism to adaptively learn the similarity of neighboring spots, and an optional cell type-aware module through integrating the pre-clustering of gene expressions. We validated STGATE on diverse spatial transcriptomics datasets generated by different platforms with different spatial resolutions. STGATE could substantially improve the identification accuracy of spatial domains, and denoise the data while preserving spatial expression patterns. Importantly, STGATE could be extended to multiple consecutive sections for reducing batch effects between sections and extracting 3D expression domains from the reconstructed 3D tissue effectively.

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Low-cost, low-input RNA-seq protocols perform nearly as well as high-input protocols

10.7287/peerj.preprints.779 ◽

2015 ◽

Author(s):

Peter A Combs ◽

Michael B Eisen

Keyword(s):

Low Cost ◽

Critical Evaluation ◽

Standard Protocol ◽

Library Preparation ◽

Rna Seq ◽

High Input ◽

Simple Modification ◽

Input Sample ◽

Low Volume ◽

The Cost

Recently, a number of protocols extending RNA-sequencing to the single-cell regime have been published. However, we were concerned that the additional steps to deal with such minute quantities of input sample would introduce serious biases that would make analysis of the data using existing approaches invalid. In this study, we performed a critical evaluation of several of these low-volume RNA-seq protocols, and found that they performed slightly less well in metrics of interest to us than a more standard protocol, but with at least two orders of magnitude less sample required. We also explored a simple modification to one of these protocols that, for many samples, reduced the cost of library preparation to approximately $20/sample.

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Effects of Spatial Transformer Location on Segmentation Performance of a Dense Transformer Network

Journal of Computational Vision and Imaging Systems ◽

10.15353/vsnl.v3i1.169 ◽

2017 ◽

Vol 3 (1) ◽

Author(s):

David Abou-Chacra ◽

John Zelek

Keyword(s):

Input Data ◽

Spatial Information ◽

Semantic Segmentation ◽

Spatial Context ◽

Class Label ◽

Spatial Span ◽

Almost All

Semantic segmentation solves the task of labelling every pixel inan image with its class label, and remains an important unsolvedproblem. While significant work has gone into using deep learningto solve this problem, almost all the existing research uses methodsthat do not make modifications on spatial context considered for thepixel being labelled. Spatial information is an important cue in taskssuch as segmentation, reusing the same spatial span for every pixeland every label may not be the best approach. Spatial TransformerNetworks have shown promising results in improving classificationperformance of existing networks by allowing networks to activelymanipulate their input data to achieve better performance. Our workshows the benefit of incorporating Spatial Transformer Networksand their corresponding decoders into networks tailored to semanticsegmentation. Our experiments show an improvement in performanceover baseline networks when using networks augmentedwith Spatial Transformers.

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Spatial molecular profiling: platforms, applications and analysis tools

Briefings in Bioinformatics ◽

10.1093/bib/bbaa145 ◽

2020 ◽

Author(s):

Minzhe Zhang ◽

Thomas Sheffield ◽

Xiaowei Zhan ◽

Qiwei Li ◽

Donghan M Yang ◽

...

Keyword(s):

Spatial Organization ◽

Spatial Information ◽

Molecular Profiling ◽

Tissue Imaging ◽

Imaging Data ◽

Tissue Heterogeneity ◽

Tissue Dissociation ◽

Recent Developments ◽

Spatial Locations

Abstract Molecular profiling technologies, such as genome sequencing and proteomics, have transformed biomedical research, but most such technologies require tissue dissociation, which leads to loss of tissue morphology and spatial information. Recent developments in spatial molecular profiling technologies have enabled the comprehensive molecular characterization of cells while keeping their spatial and morphological contexts intact. Molecular profiling data generate deep characterizations of the genetic, transcriptional and proteomic events of cells, while tissue images capture the spatial locations, organizations and interactions of the cells together with their morphology features. These data, together with cell and tissue imaging data, provide unprecedented opportunities to study tissue heterogeneity and cell spatial organization. This review aims to provide an overview of these recent developments in spatial molecular profiling technologies and the corresponding computational methods developed for analyzing such data.

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Comparative analysis of sequencing technologies platforms for single-cell transcriptomics

10.1101/463117 ◽

2018 ◽

Cited By ~ 1

Author(s):

Kedar Nath Natarajan ◽

Zhichao Miao ◽

Miaomiao Jiang ◽

Xiaoyun Huang ◽

Hongpo Zhou ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Single Cells ◽

K562 Cells ◽

Library Preparation ◽

Rna Seq ◽

Illumina Hiseq ◽

Technical Variability ◽

Sequencing Technologies ◽

Sequencing Platforms

AbstractAll single-cell RNA-seq protocols and technologies require library preparation prior to sequencing on a platform such as Illumina. Here, we present the first report to utilize the BGISEQ-500 platform for scRNA-seq, and compare the sensitivity and accuracy to Illumina sequencing. We generate a scRNA-seq resource of 468 unique single-cells and 1,297 matched single cDNA samples, performing SMARTer and Smart-seq2 protocols on mESCs and K562 cells with RNA spike-ins. We sequence these libraries on both BGISEQ-500 and Illumina HiSeq platforms using single- and paired-end reads. The two platforms have comparable sensitivity and accuracy in terms of quantification of gene expression, and low technical variability. Our study provides a standardised scRNA-seq resource to benchmark new scRNA-seq library preparation protocols and sequencing platforms.

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Changes in the experience of time: The impact of spatial information on the perception and memory of duration

Quarterly Journal of Experimental Psychology ◽

10.1177/1747021820968492 ◽

2020 ◽

pp. 174702182096849

Author(s):

Can Fenerci ◽

Kevin da Silva Castanheira ◽

Myles LoParco ◽

Signy Sheldon

Keyword(s):

Spatial Information ◽

Spatial Context ◽

Cognitive Mechanisms ◽

Time Duration ◽

Event Segmentation ◽

Event Segmentation Theory ◽

Experience Of Time ◽

The Impact ◽

New Evidence ◽

Encoding And Retrieval

Although it is understood that our experience of time is fluid and subjective, the cognitive mechanisms underlying this phenomenon are not well described. Based on event segmentation theory, we tested the hypothesis that changes in the context, particularly the spatial context, of an experience impact how an individual perceives (encodes) and remembers the length of that event. A group of participants viewed short videos of scenes from movies that either contained shifts in spatial context (e.g., characters moving through doorways) or did not contain any shifts in spatial context. In one task, participants estimated a randomly selected time duration (between 10 and 23 s) when encoding these videos. In a second task, the same participants estimated the duration of the videos after viewing them. We found that even though the presence of spatial shifts impacted how time was perceived, the nature of this effect differed as a function of task. Specifically, when time was estimated at encoding, these estimates were longer for videos that did not contain spatial shifts compared with those with spatial shifts. However, when these estimates were made at retrieval, durations were reported as longer for videos with spatial context shifts than those without. A second experiment replicated these main findings in a new sample. We interpret these results as providing new evidence for theories on how context changes, particularly those in spatial information, distort the experience of time differently during the encoding and retrieval phases of memory.

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Relational processing demands and the role of spatial context in the construction of episodic simulations

10.31234/osf.io/rp8x7 ◽

2019 ◽

Author(s):

Kristina Wiebels ◽

Donna Rose Addis ◽

David Moreau ◽

Valerie van Mulukom ◽

Kelsey Esmé Onderdijk ◽

...

Keyword(s):

Spatial Information ◽

Spatial Context ◽

Relational Processing ◽

Theoretical Frameworks ◽

Episodic Simulation ◽

Processing Demands ◽

Future Events ◽

Event Representations ◽

First Time

Reports on differences between remembering the past and imagining the future have led to the hypothesis that constructing future events is a more cognitively demanding process. However, factors that influence these increased demands, such as whether the event has been previously constructed and the types of details comprising the event, have remained relatively unexplored. Across two experiments, we examined how these factors influence the process of constructing event representations by having participants repeatedly construct events and measuring how construction times and a range of phenomenological ratings changed across time points. In Experiment 1, we contrasted the construction of past and future events and found that, relative to past events, the constructive demands associated with future events are particularly heightened when these events are imagined for the first time. Across repeated simulations, future events became increasingly similar to past events in terms of construction times and incorporated detail. In Experiment 2, participants imagined future events involving two memory details (person, location) and then reimagined the event either i) exactly the same, ii) with a different person, or iii) in a different location. We predicted that if generating spatial information is particularly important for event construction, a change in location will have the greatest impact on constructive demands. Results showed that spatial context contributed to these heightened constructive demands more so than person details, consistent with theories highlighting the central role of spatial processing in episodic simulation. We discuss the findings from both studies in the light of relational processing demands and consider implications for current theoretical frameworks.

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