Phylodynamics for cell biologists

Science ◽  
2021 ◽  
Vol 371 (6526) ◽  
pp. eaah6266
Author(s):  
T. Stadler ◽  
O. G. Pybus ◽  
M. P. H. Stumpf
Keyword(s):  
Single Cell ◽  
Cell Biology ◽  
Statistical Hypothesis ◽  
Statistical Hypothesis Testing ◽  
Level Data ◽  
Technical Advances ◽  
Multicellular Organisms ◽  
Cell Data ◽  
Dynamical Information ◽  
Theoretical Developments

Multicellular organisms are composed of cells connected by ancestry and descent from progenitor cells. The dynamics of cell birth, death, and inheritance within an organism give rise to the fundamental processes of development, differentiation, and cancer. Technical advances in molecular biology now allow us to study cellular composition, ancestry, and evolution at the resolution of individual cells within an organism or tissue. Here, we take a phylogenetic and phylodynamic approach to single-cell biology. We explain how “tree thinking” is important to the interpretation of the growing body of cell-level data and how ecological null models can benefit statistical hypothesis testing. Experimental progress in cell biology should be accompanied by theoretical developments if we are to exploit fully the dynamical information in single-cell data.

scholarly journals Normalisr: normalization and association testing for single-cell CRISPR screen and co-expression

2021 ◽  
Author(s):  
Lingfei Wang
Keyword(s):  
Single Cell ◽  
Regulatory Networks ◽  
Large Scale ◽  
High Sensitivity ◽  
Statistical Hypothesis ◽  
P Value ◽  
Statistical Hypothesis Testing ◽  
Experimental Conditions ◽  
Library Size ◽  
Association Testing

AbstractSingle-cell RNA sequencing (scRNA-seq) provides unprecedented technical and statistical potential to study gene regulation but is subject to technical variations and sparsity. Here we present Normalisr, a linear-model-based normalization and statistical hypothesis testing framework that unifies single-cell differential expression, co-expression, and CRISPR scRNA-seq screen analyses. By systematically detecting and removing nonlinear confounding from library size, Normalisr achieves high sensitivity, specificity, speed, and generalizability across multiple scRNA-seq protocols and experimental conditions with unbiased P-value estimation. We use Normalisr to reconstruct robust gene regulatory networks from trans-effects of gRNAs in large-scale CRISPRi scRNA-seq screens and gene-level co-expression networks from conventional scRNA-seq.

scholarly journals Doing nothing and what it looks like: inactivity in fattening cattle

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9395
Author(s):  
Sara Hintze ◽  
Freija Maulbetsch ◽  
Lucy Asher ◽  
Christoph Winckler
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Statistical Hypothesis ◽  
Machine Learning Algorithm ◽  
Body Parts ◽  
Group Level ◽  
Statistical Hypothesis Testing ◽  
Level Data ◽  
Eyes Open ◽  
Husbandry System

Background Animals kept in barren environments often show increased levels of inactivity and first studies indicate that inactive behaviour may reflect boredom or depression-like states. However, to date, knowledge of what inactivity looks like in different species is scarce and methods to precisely describe and analyse inactive behaviour are thus warranted. Methods We developed an Inactivity Ethogram including detailed information on the postures of different body parts (Standing/Lying, Head, Ears, Eyes, Tail) for fattening cattle, a farm animal category often kept in barren environments. The Inactivity Ethogram was applied to Austrian Fleckvieh heifers kept in intensive, semi-intensive and pasture-based husbandry systems to record inactive behaviour in a range of different contexts. Three farms per husbandry system were visited twice; once in the morning and once in the afternoon to cover most of the daylight hours. During each visit, 16 focal animals were continuously observed for 15 minutes each (96 heifers per husbandry system, 288 in total). Moreover, the focal animals’ groups were video recorded to later determine inactivity on the group level. Since our study was explorative in nature, we refrained from statistical hypothesis testing, but analysed both the individual- and group-level data descriptively. Moreover, simultaneous occurrences of postures of different body parts (Standing/Lying, Head, Ears and Eyes) were analysed using the machine learning algorithm cspade to provide insight into co-occurring postures of inactivity. Results Inspection of graphs indicated that with increasing intensity of the husbandry system, more animals were inactive (group-level data) and the time the focal animals were inactive increased (individual-level data). Frequently co-occurring postures were generally similar between husbandry systems, but with subtle differences. The most frequently observed combination on farms with intensive and semi-intensive systems was lying with head up, ears backwards and eyes open whereas on pasture it was standing with head up, ears forwards and eyes open. Conclusion Our study is the first to explore inactive behaviour in cattle by applying a detailed description of postures from an Inactivity Ethogram and by using the machine learning algorithm cspade to identify frequently co-occurring posture combinations. Both the ethogram created in this study and the cspade algorithm may be valuable tools in future studies aiming to better understand different forms of inactivity and how they are associated with different affective states.

scholarly journals Understanding angiodiversity: insights from single cell biology

Development ◽  
2020 ◽  
Vol 147 (15) ◽  
pp. dev146621 ◽  
Author(s):  
Moritz Jakab ◽  
Hellmut G. Augustin
Keyword(s):  
Endothelial Cells ◽  
Single Cell ◽  
Cell Biology ◽  
Vessel Wall ◽  
Single Cells ◽  
Whole Body ◽  
Molecular Dissection ◽  
Organ Specific ◽  
Technical Advances ◽  
Dynamic Components

ABSTRACTBlood vessels have long been considered as passive conduits for delivering blood. However, in recent years, cells of the vessel wall (endothelial cells, smooth muscle cells and pericytes) have emerged as active, highly dynamic components that orchestrate crosstalk between the circulation and organs. Encompassing the whole body and being specialized to the needs of distinct organs, it is not surprising that vessel lining cells come in different flavours. There is calibre-specific specialization (arteries, arterioles, capillaries, venules, veins), but also organ-specific heterogeneity in different microvascular beds (continuous, discontinuous, sinusoidal). Recent technical advances in the field of single cell biology have enabled the profiling of thousands of single cells and, hence, have allowed for the molecular dissection of such angiodiversity, yielding a hitherto unparalleled level of spatial and functional resolution. Here, we review how these approaches have contributed to our understanding of angiodiversity.

scholarly journals BABEL enables cross-modality translation between multi-omic profiles at single-cell resolution

2020 ◽  
Author(s):  
Kevin E. Wu ◽  
Kathryn E. Yost ◽  
Howard Y. Chang ◽  
James Zou
Keyword(s):  
Single Cell ◽  
Cell Biology ◽  
Data Exploration ◽  
Grand Challenge ◽  
Fine Grained ◽  
Powerful Approach ◽  
Technical Innovations ◽  
Cell Data ◽  
Human And Mouse

AbstractSimultaneous profiling of multi-omic modalities within a single cell is a grand challenge for single-cell biology. While there have been impressive technical innovations demonstrating feasibility – for example generating paired measurements of scRNA-seq and scATAC-seq – wide-spread application of joint profiling is challenging due to the experimental complexity, noise, and cost. Here we introduce BABEL, a deep learning method that translates between the transcriptome and chromatin profiles of a single cell. Leveraging a novel interoperable neural network model, BABEL can generate scRNA-seq directly from a cell’s scATAC-seq, and vice versa. This makes it possible to computationally synthesize paired multi-omic measurements when only one modality is experimentally available. Across several paired scRNA-seq and scATAC-seq datasets in human and mouse, we validate that BABEL accurately translates between these modalities for individual cells. BABEL also generalizes well to new biological contexts not seen during training. For example, starting from scATAC-seq of patient derived basal cell carcinoma (BCC), BABEL generated scRNA-seq that enabled fine-grained classification of complex cell states, despite having never seen BCC data. These predictions are comparable to analyses of the experimental BCC scRNA-seq data. We further show that BABEL can incorporate additional single-cell data modalities, such as CITE-seq, thus enabling translation across chromatin, RNA, and protein. BABEL offers a powerful approach for data exploration and hypothesis generation.

scholarly journals Information Theory and Stem Cell Biology

2017 ◽  
Author(s):  
Rosanna C. G. Smith ◽  
Ben D. MacArthur
Keyword(s):  
Information Theory ◽  
Stem Cell ◽  
Single Cell ◽  
Cell Biology ◽  
Cell Dynamics ◽  
Cell Behaviour ◽  
Single Cell Profiling ◽  
Cell To Cell Variability ◽  
Near Future ◽  
Cell Data

AbstractPurpose of ReviewTo outline how ideas from Information Theory may be used to analyze single cell data and better understand stem cell behaviour.Recent findingsRecent technological breakthroughs in single cell profiling have made it possible to interrogate cell-to-cell variability in a multitude of contexts, including the role it plays in stem cell dynamics. Here we review how measures from information theory are being used to extract biological meaning from the complex, high-dimensional and noisy datasets that arise from single cell profiling experiments. We also discuss how concepts linking information theory and statistical mechanics are being used to provide insight into cellular identity, variability and dynamics.SummaryWe provide a brief introduction to some basic notions from information theory and how they may be used to understand stem cell identities at the single cell level. We also discuss how work in this area might develop in the near future.

scholarly journals 12 Grand Challenges in Single-Cell Data Science

2019 ◽  
Author(s):  
David Laehnemann ◽  
Johannes Köster ◽  
Ewa Szczurek ◽  
Davis J McCarthy ◽  
Stephanie C Hicks ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Biology ◽  
Data Science ◽  
Single Cell Analysis ◽  
State Of The Art ◽  
Cell Analysis ◽  
Open Problems ◽  
Single Cell Sequencing ◽  
Current State ◽  
Cell Data

The recent upswing of microfluidics and combinatorial indexing strategies, further enhanced by very low sequencing costs, have turned single cell sequencing into an empowering technology; analyzing thousands—or even millions—of cells per experimental run is becoming a routine assignment in laboratories worldwide. As a consequence, we are witnessing a data revolution in single cell biology. Although some issues are similar in spirit to those experienced in bulk sequencing, many of the emerging data science problems are unique to single cell analysis; together, they give rise to the new realm of 'Single-Cell Data Science'. Here, we outline twelve challenges that will be central in bringing this new field forward. For each challenge, the current state of the art in terms of prior work is reviewed, and open problems are formulated, with an emphasis on the research goals that motivate them. This compendium is meant to serve as a guideline for established researchers, newcomers and students alike, highlighting interesting and rewarding problems in 'Single-Cell Data Science' for the coming years.

scholarly journals 12 Grand challenges in single-cell data science

2019 ◽  
Author(s):  
David Laehnemann ◽  
Johannes Köster ◽  
Ewa Szcureck ◽  
Davis McCarthy ◽  
Stephanie C Hicks ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Biology ◽  
Data Science ◽  
Single Cell Analysis ◽  
State Of The Art ◽  
Cell Analysis ◽  
Open Problems ◽  
Single Cell Sequencing ◽  
Current State ◽  
Cell Data

The recent upswing of microfluidics and combinatorial indexing strategies, further enhanced by very low sequencing costs, have turned single cell sequencing into an empowering technology; analyzing thousands—or even millions—of cells per experimental run is becoming a routine assignment in laboratories worldwide. As a consequence, we are witnessing a data revolution in single cell biology. Although some issues are similar in spirit to those experienced in bulk sequencing, many of the emerging data science problems are unique to single cell analysis; together, they give rise to the new realm of 'Single Cell Data Science'. Here, we outline twelve challenges that will be central in bringing this new field forward. For each challenge, the current state of the art in terms of prior work is reviewed, and open problems are formulated, with an emphasis on the research goals that motivate them. This compendium is meant to serve as a guideline for established researchers, newcomers and students alike, highlighting interesting and rewarding problems in 'Single Cell Data Science' for the coming years.

scholarly journals 12 Grand Challenges in Single-Cell Data Science

2019 ◽  
Author(s):  
David Laehnemann ◽  
Johannes Köster ◽  
Ewa Szczurek ◽  
Davis J McCarthy ◽  
Stephanie C Hicks ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Biology ◽  
Data Science ◽  
Single Cell Analysis ◽  
State Of The Art ◽  
Cell Analysis ◽  
Open Problems ◽  
Single Cell Sequencing ◽  
Current State ◽  
Cell Data

The recent upswing of microfluidics and combinatorial indexing strategies, further enhanced by very low sequencing costs, have turned single cell sequencing into an empowering technology; analyzing thousands—or even millions—of cells per experimental run is becoming a routine assignment in laboratories worldwide. As a consequence, we are witnessing a data revolution in single cell biology. Although some issues are similar in spirit to those experienced in bulk sequencing, many of the emerging data science problems are unique to single cell analysis; together, they give rise to the new realm of 'Single-Cell Data Science'. Here, we outline twelve challenges that will be central in bringing this new field forward. For each challenge, the current state of the art in terms of prior work is reviewed, and open problems are formulated, with an emphasis on the research goals that motivate them. This compendium is meant to serve as a guideline for established researchers, newcomers and students alike, highlighting interesting and rewarding problems in 'Single-Cell Data Science' for the coming years.

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