Chapter 1
Emerging techniques in biological
sciences
Ranbir Chander Sobti1, Ahmad Ali2, Phuntsog Dolma2, Anuragini Kadwalia2, Tundup Dolma3, Jagdish Rai4
and Archana Chauhan2
1
Department of Biotechnology, Panjab University, Chandigarh, India, 2Department of Zoology, Panjab University, Chandigarh, India, 3Department of
Environmental Studies, Panjab University, Chandigarh, India, 4Institute of Forensic Science & Criminology (U.I.E.A.S.T.), Panjab University,
Chandigarh, India
1.1
Introduction
The term “Bioscience” refers to any field of study that deals with living organisms including plants, animals, and microorganisms. Modern biology is a wide and diverse area that studies the structure, function, growth, distribution, evolution, and other characteristics of living organisms through a variety of specialized disciplines. In the modern era, the
definition of this word can be expanded to include all the work done in biological fields to benefit mankind and other
organisms. This includes all the progress made to date in the fields of health sciences, animal sciences, genetics, molecular biology, biochemistry, bioinformatics etc. The term “biotechnology” refers to the application of techniques and the
use of living organisms or biological processes to develop agricultural, industrial, or medical products. In particular,
DNA technology is used in many aspects of modern biotechnology including DNA sequencing, analysis, cutting, and
pasting. Using cellular and bimolecular processes, this biology-based research produces products and technology that
improve our lives and the health of our world. Science and technology support each other in the development of new
technologies, while scientific knowledge is used to produce new technologies. Scientists frequently use modern technologies to investigate new things and assist in the exploration of nature. In medicine and healthcare, digital technology
helps in the transformation of an unsustainable healthcare system into one that is sustainable. These are technologies
that are driven by efforts to acquire or synthesize newer biological or molecular diversity, or a wider range of specificity, so that the user can pick and choose what is valuable from a broad pool of newly obtained variation. The importance of biosciences is more prominent in present times considering scientists successfully discovered a vaccine against
SARS-Covid19 in a very limited time. This was achieved because of advanced technology. The need for technology in
bioscience is to generate a large amount of yield from agriculture and industries, and discovering new things as the traditional way of production and also generating environmentally friendly, pest-resistance products was not effective. The
breakthrough discoveries in biology were made by people trained in techniques from other disciplines. Many of the
Nobel prizes in chemistry are awarded for discoveries related to biological systems. Physicists have developed techniques that have advanced the experimental science of every discipline. Mathematics is considered the mother of science,
and nowadays, computational science, along with statistics, is leading the measure of developments in every sphere of
science and business. Many types of equipment used in biological sciences were devised by engineers. When all these
discipline areas work together for a common goal, the result is bound to be fruitful. Many technological advancements
has been made so far. Some of the techniques are discussed below.
1.2
Artificial Intelligence
It is very important to discuss “Artificial Intelligence” first of all because the majority of the advanced technologies
used in biosciences generate output are based on this technique. The word “Artificial” refers to something that is not
natural and the word “Intelligence” can be defined as the ability to receive and analyze information, store it in the
Advances in Animal Experimentation and Modeling. DOI: https://doi.org/10.1016/B978-0-323-90583-1.00013-1
© 2022 Elsevier Inc. All rights reserved.
3
4
PART | A Modern technological advancements in the field of animal experimentation
form of memory, and apply this knowledge for future actions. Human beings are the most intelligent of all the organisms known though higher animals, for examples, mammals show intelligent behavior. Lower animals and plants are
also known to show a subtle level of intelligence. Living organisms display “Natural Intelligence.” However, the
term “Artificial Intelligence” is used for the machines that are programmed to think and act independent of human
intervention using the previous information stored as memory. Artificial intelligence has two major subtypesmachine learning and deep learning. Machine learning allows the machine to learn from previous data and perform a
function accordingly. Deep learning works similar to machine learning but it uses many layers to analyze data just
like our neurons in the nervous system form complex networks. This allows the machine to analyze large volumes of
data and act accordingly. These machines can make predictions, recommendations, create visuals, differentiate different types of cells and perform many other functions which make them useful to the modern biologists. Artificial
Intelligence is used these days almost in every branch of biology, health and medicine. Machine learning is used in
the next generation sequencing and structural predictions of proteins. Deep learning can be used in generating
images, genome analysis, prediction of genetic variants, finding sites of mutations and drug discovery. New Drugs
can be designed by using computer aided drug designing as artificial intelligence can help in virtual screening of the
drugs, quantitative structure activity relation analysis and in silico analysis of absorption, distribution, metabolism,
excretion and toxicity properties (Zhong et al., 2018). Biomarkers can be identified using deep neural network
(Mamoshina, Vieira, Putin, & Zhavoronkov, 2016). Diseases can be diagnosed and even pre diagnosed effectively
without using deep neural networks and this way health care facilities can be made available even in the remote
places where health care professionals are not available.
1.3
Imaging cells to molecules in 3D
Imaging is one of the most important tools for biologists or scientists to evaluate and record size and form and structures, various anomalies, variation, etc. Max Knoll and Ernst Ruska at the Berlin Technische Hochschule in 1931
invented electron microscopy with higher resolution barrier. In the 19th century the theoretical and technical groundworks of the modern light microscope was created, most notably by keeping the diffraction-limit theory into consideration, but also aberration-corrected lenses and an optimized illumination mode called Köhler illumination (Köhler,
1894) were applied. Electron microscopy enabled the visualization of biological structures at a resolution of 0.1 nm.
And one of the greatest challenges in imaging biological samples is their inherently low contrast, which is due to their
refractive index being very close to water. Thus, generating little scatter interaction with the incident light. Various different methods for increasing contrast have been developed including phase imaging and polarization changes, staining
and fluorescence. Out of these the staining and fluorescence being possibly the most far-reaching development since
the invention of the light microscope (Wollman, Nudd, Hedlund, & Leake, 2015).
Another improved imaging tool is Cryo-electron microscopy which forms a promising tool for determining the structure
of proteins for which X-ray crystallography or nuclear magnetic resonance (NMR) is not feasible (Zhang, Minary, &
Levitt, 2012). Some proteins like membrane proteins are not easy to crystalize and therefore the structure determination
through X-ray crystallography is not feasible. These membrane proteins are also not suitable for NMR-spectroscopy based
structure determination because it requires high purity and concentration of protein. Also, cryo electron microscopy made
the very important discovery of lipid bilayer structure of cell membrane possible. Fluorescence-based confocal microscopy
is used today to visualize the biological samples at molecular precision in 3D. It has confirmed the localization of molecules in subcellular organelles and other spatiotemporal changes in living systems (Table 1.1). Light Sheet Fluorescence
Microscopy combined with advanced optics can be used to image cells at subcellular levels in their native state (Liu et al.,
2018). Human chromosomes are prepared in a linear and conserved sequence order which undergoes additional spatial folding within the three-dimensional space of the nucleus. While, structural variations in this organization are an important
source of natural genetic diversity, though the cytogenetic anomalies can also underlie a number of human diseases and disorders (Hu, Maurais, & Ly, 2020). Thus, there is a need for methods that can image chromosomes with genome-wide coverage, as well as greater genomic and optical resolution. Medical imaging is a branch of science dealing with the
visualization of the internal organs of the body without using invasive techniques starting from X-ray photography of bones,
ultrasound imaging of internal organs, MRI of the brain, fluorescent tracer-dye based angiography, etc. has enabled precise
diagnosis of ailments. Medical imaging (X-ray, MRI, ultrasounds, etc.) is used to create visual representations of the interior
of the body for clinical analysis and medical intervention of complex diseases in a short period of time. Fourier-transform
infrared spectroscopy-imaging can visualize the concentration of metabolites and macromolecules in cells of a tissue, using
false-color rendering (Kumar, Srinivasan, & Nikolajeff, 2018). This technique is expected to be a cost-effective cytological
diagnosis tool in the future. Digital Pathology is another useful technique in which histological glass slides are converted
TABLE 1.1 Brief summary of various microscope used for the identification or analysis of specimen to cell or gene.
Microscope
Inventor
Method
Application
Reference
Compound
microscope
Galileo 1609
Used an objective lens to collect
light from a specimen and a second
lens to magnify the image
Magnify the image or specimen
Wollman et al.
(2015)
Compound
microscope
Robert Hooke
and Antonj van
Leeuwenhoek
1665
Light passes through 2 lenses and
can magnify up to 2000 3
Microbiology and microscopic
structure of cork, seeds, skin, fish
scales, oyster shell, the white
matter upon the tongues of feverish
persons, nerves, muscle fibers, fish
circulatory system, insect eyes,
spider physiology, mite
reproduction, aquatic plants
Gest (2009)
Electron
microscope
Max Knoll and
Ernst Ruska
1931
Uses beams of electrons rather than
light
Used to observe minute objects:
viruses, DNA, parts of cells
Wollman et al.
(2015)
Phase contrast
microscopy
Fritz Zernike
1934
Achieved by manipulating the
transmitted, background light
differently from the scattered light,
which is typically phase-shifted 90
by the sample. A circular annulus is
placed in front of the light source,
producing a ring of illumination
with much higher contrast image
Microscopy technique in cell
culture, thin tissue slices,
lithographic patterns, fibers and live
cell imaging in their natural state
without previous fixation or
labeling
Zernike (1955)
Cryo-electron
microscopy
Jacques
Dubochet,
Joachim Frank
and Richard
Henderson
1930s
A small aliquot of sample in
solution or suspension is applied to
an electron microscopy grid and is
blotted to a thin layer and
immediately plunged into liquid
ethane (around 2180 C), so that
the molecules get trapped in a layer
of vitrified water
Determining the structure of
proteins in 3D
Dubochet et al.
(1988)
Differential
interference
contrast
Georges
Nomarski 1955
Use of a Nomarski Wollaston
prism through which polarized light
is sheared into two beams
polarized at 90 degrees to each
other
Imaging unstained microbiological
samples to reveal the boundaries of
cells and subcellular organelles
Nomarski and
Weill (1955)
Fluorescence
microscope
Otto Heimstaedt
and Heinrich
Lehmann
1911 13
Uses fluorescence and
phosphorescence
To study properties of organic or
inorganic substances
Rost (1992)
Epifluorescence
microscopes
Johan S. Ploem
Excitation of the fluorophore and
detection of the fluorescence are
done through the same light path
(i.e., through the objective)
Useful when imaging thick
samples, over 10 µm deep
Böhmer and
Enderlein
(2003)
Confocal
microscope
Marvin Minsky
1955
High-resolution imaging of thick
specimens (without physical
sectioning) can be analyzed using
fluorescent-labeled dye
Assessment of different types of eye
diseases, particularly imaging,
qualitative analysis, and
quantification of endothelial cells of
the cornea. Also used as the data
retrieval mechanism in some 3D
optical data storage systems
Hara,
Morishige,
Chikama, and
Nishida (2003)
Multiphoton
microscope
Winfried Denk
and James
Strickler 1990
Multiphoton fluorescence
excitation results in the capture of
high-resolution three-dimensional
images of specimen tagged with
highly specific fluorophores
Use for imaging living, intact
biological tissues on the length
scale
Hoover and
Squier (2013)
(Continued )
6
PART | A Modern technological advancements in the field of animal experimentation
TABLE 1.1 (Continued)
Microscope
Inventor
Method
Application
Reference
Total internal
reflection
fluorescence
(TIRF)
microscope
Daniel Axelrod
early 1980s
Have limited specimen region in
the field very adjacent to the
interface between two media
having different refractive indices
Use in biochemistry and cell
biology for visualization of cell,
tracking of secretory granules etc.
Axelrod
(2001), Fish
(2009)
Brillouin
microscopy
Léon Brillouin
Uses a low-power, focused laser
beam and a high-resolution
confocal spectrometer to measure
the Brillouin frequency
Use to create 3D structures without
even labeling the biological
samples
Edmondson,
Broglie,
Adcock, and
Yang (2014)
CRISPR labeled
fluorescence
Emmanuelle
Charpentier and
Jennifer Doudna
CRISPR labeling and imaging of
protein-coding genes in living cells
Use for imaging gene loci in vivo
Chen, Zou, Xu,
Liang, and
Huang (2018),
Nasri et al.
(2019)
into images which can further be analyzed by using deep neural networks. El Achi and Khoury (2020) recently summarized
many such techniques which help in detecting hematological disorders especially cancer. The capacity of Artificial
intelligence-based techniques in detecting molecular and genetic alterations in hepatic and gastro intestinal cancers from
histological digital slides has lately been reported (Calderaro & Kather, 2021). Ever since the development of imagining
from light microscope to advanced fluorescence microscopy has motivated by aspirations to illuminate and understand the
functional anatomy of the cell. Thereby, 3D microscopy has permitted fundamental insight into the steady-state organization of a cell and its dynamics as well as the structure, turnover, mobility, and function of its components (Renz, 2013).
1.4
Microarray
Microarrays are the miniature version of complex laboratory tools which is why they are also known as lab on chip.
DNA Microarray or DNA chip or biochip comprises of the oligonucleotides attached on solid surfaces like glass slides.
Advanced photolithography techniques made it possible to attach an oligonucleotide probe on a glass slide or other
solid surfaces. Therefore, millions of oligonucleotide probes can be packed on a glass slide and all mRNA products
from a tissue can be quantitatively detected at the same time using only a small sample. This is in principle southern
blotting at large-scale miniaturized form. The mRNA is detected after converting it to DNA by reverse transcription
and also attaching a fluorophore to it. These can be used to detect mutations, single nucleotide polymorphism (SNPs)
and detection of binding site clusters by CHIP-chip sequencing. Using Artificial Intelligence, thousands of DNA Spots
can be analyzed simultaneously. Many modifications of these techniques are used to diagnose various diseases. These
can be used for detection of cancers and infectious diseases. Scientists at Technical University of Munich in Germany
have very recently developed a rapid microarray test for identifying antibodies against SARS-Cov-2 (Hastings, 2021).
RNA microarrays are used for studying various aspects of transcriptomics. Protein microarrays help in studying gene
expression and proteomics (Fig. 1.1).
1.5
Genetic engineering with precision
Genetic engineering is a method of altering genetic makeup in order to get the desired outcome. Modifications like
(insertions, deletions, and substitutions) in a living organism’s genome are referred to as genome engineering, genome
editing, and gene editing, which combine science, technology, and engineering to comprehend, develop, redesign, manufacture, and modify genetic materials in living creatures and biological systems more quickly (Sandler, 2020).
Engineered gene drives are a type of genetic alteration that improve the potential of a genetic characteristic or element
being inherited more frequently than the Mendelian ratio. Engineered gene drives, which stimulate the biased inheritance of specific genes, have the potential to disseminate beneficial genes throughout wild populations or inhibit hazardous species, and could be especially useful in the control of vector-borne diseases like malaria, eliminating insect-borne
diseases, eradicating invasive foreign species, and even reversing pesticide and herbicide resistance in a cost-effective
Emerging techniques in biological sciences Chapter | 1
7
FIGURE 1.1 Applications of DNA microarray
technology.
FIGURE 1.2 Genetic engineering process.
and ecologically sound manner (Champer, Buchman, & Akbari, 2016). Plant genetic engineering techniques have been
created to make plant breeding faster, more predictable, and adaptable to a wide range of species, for example, Bt cotton, Bt brinjal. The basic steps of genetic engineering are illustrated in (Fig. 1.2).
The RNAi (RNA interference) was discovered by Fire and Mello in 1960. The phenomenon of RNAi is engaged in
sequence-specific gene regulation, which is triggered by the introduction of dsRNA, which inhibits translation or suppresses
8
PART | A Modern technological advancements in the field of animal experimentation
transcription. Antisense technology is less precise, efficient, and stable than RNAi technology. It has been effectively used to
change the expression of genes in plants to improve quality attributes. RNAi is mediated by 21 23 nucleotide small interfering RNAs (siRNAs), which are generated by the RNAse II-like enzyme Dicer from lengthy double-stranded RNAs. The
siRNAs are then added to an RNA-induced silencing complex (RISC), which identifies and cleaves mRNA that is complementary to the siRNAs (Campbell & Choy, 2005). A ribonuclease known as DICER or Dicer-like enzyme does the cleavage.
Dicer enzymes are double strand (ds)-specific RNases whose cleavage products are used to provide specificity in RNAbased gene-silencing pathways (Pare & Hobman, 2007). Scientists can easily knock out a gene product from an organism
using RNAi, whereas the earlier methods of gene knockout were time-consuming as well as unfeasible (Saurabh, Vidyarthi,
& Prasad, 2014). According to central dogma, any gene is transcribed as an mRNA, which then translates into a protein,
which impacts the organism’s phenotype. RNAi disrupts the gene-to-phenotype sequence by degrading specific mRNA transcribed from the target gene. The RNA RISC, which is activated by double-stranded RNA, causes sequence-specific mRNA
degradation in the RNA-interference pathway (Sontheimer, 2005). Therefore, in principle, any gene can be silenced by adding a small RNA to the cell that is complementary to the target gene.
The most modern method of genetic engineering is genome editing using programmable endonucleases.
Endonucleases are employed to cause double strand breaks (DSBs) in certain target genes. The cellular DNA repair
mechanism then uses nonhomologous end joining or homology-directed repair to repair the double strand break (HDR).
Insertions, deletions, substitutions, and DNA recombination may occur during the process (Puchta, 2005; Puchta,
Dujon, & Hohn, 1996; Symington & Gautier, 2011). Site-directed mutagenesis (SDM) and other genetic engineering
approaches can be used to investigate protein structure, both chemical and three-dimensional, as well as flexibility and
function. Unlike chemical and physical mutagenesis, which introduces numerous mutations across the genome at random, SDM approaches are designed to be target specific (Quétier, 2016). The Zinc finger nuclease technology (ZFN),
TAL effector nucleases (TALEN) and clustered regularly interspaced short palindromic repeat (CRISPR) and CRISPRassociated protein 9 (Cas9) are the main SDM techniques currently in use (Schiml & Puchta, 2016). In comparison to
ZFN and TALEN, CRISPR-Cas9 has attracted a lot of interest recently since it is a more user-friendly and costeffective method of creating target-specific constructs.
Various techniques are used for changing the DNA of an organism, starting from random mutagenesis to precise
changes using CRISPR/Cas9 (Clustered regularly interspaced short palindromic repeats) gene editing, which is mostly
used in genomic editing approach. The CRISPR/Cas9 comprises of a Cas9 nuclease and a single-guide RNA for
genome editing (sgRNA). Cas9 can recognize and cut a specific target DNA sequence with the help of sgRNA, resulting in DSBs that cause cell repair processes and mutations at or around the DSB locations (Bao et al., 2019). Numerous
guide sequences can be encoded into a single CRISPR array to allow simultaneous editing of multiple places throughout
the mammalian genome, exhibiting the RNA-guided nuclease technology’s programmability and wider application
(Cong et al., 2013). This method is applicable to systems ranging from cells in vitro to animals in vivo. CRISPR/Cas9
genome editing has been utilized to fix disease-causing DNA mutations ranging from a single base pair to massive deletions (Cai, Fisher, Huang, & Xie, 2016). CRISPR-Cas9 has been utilized to improve yield, quality, and nutritional value
in a growing number of monocot and dicot plant species, as well as to introduce or enhance resistance to biotic and abiotic challenges, among other things. Despite worries about biosafety, genome editing is a promising technology that has
the potential to contribute to food production for the benefit of the world’s rising population (El-Mounadi et al., 2020).
Enzymes for DNA recombination are also used to replace a target DNA piece in the genome. These precision genetic
engineering techniques are very valuable for crop improvement and the treatment of genetic diseases in humans (Cai
et al., 2016). CRISPR technology can detect the DNA/RNA of a pathogen by binding to it through complementary
guide RNA and giving fluorescence signals while cleaving the target polynucleotide. The feasibility of using the
CRISPR-Cas system to combat viral attacks has also been evaluated (Konwarh, 2020). CRISPR-cas9 based genome
editing in stem cells opens up whole new possibilities for treatment and research models of disease (Valenti, Serena,
Carbonare, & Zipeto, 2019). Induced pluripotent stem cells can be created from any differentiated cell by expression of
only 4 factors: Oct3/4, Sox2, c-Myc, and Klf4 (Takahashi & Yamanaka, 2006). These combinations of techniques are
already being tried for genetic therapy of sickle cell anemia (Park & Bao, 2021).
Systems biology (SB) is a branch of biology that focuses on these complex processes at the cellular level (Chen,
Wang, & Zhang, 2009). SB is a new interdisciplinary discipline that aims to investigate and comprehend the complex
behavior of biological systems at the system level rather than at the level of individual components. This is accomplished by continuously combining experimental data with known system information. To analyze the system and use
the generated data, new systems biology and bioinformatics models and algorithms are required (Jayavelu, 2015). In a
system biology approach, designed genetic networks and genes are constructed from component pieces available at
a repository called iGEM (International Genetically Engineered Machine). This repository has DNA pieces available in
Emerging techniques in biological sciences Chapter | 1
9
a format that the components can be joined in any design because a convention is followed to provide restriction sites
in these DNA elements. The repository has a very rich catalog of promoters, reporters, regulatory elements, etc. These
DNA components (also called BioBricks) are supplied to member labs at a nominal charge in the spirit of open science
(Wang et al., 2021). In an engineering approach to biological systems, cell-free systems are used to synthesize proteins
and metabolites using the necessary components of cellular machinery rather than the live cell (Rollin, Tam, & Zhang,
2013). These necessary components for synthesis or other chemical reactions are extracted from cells using separation
techniques such as ultracentrifugation. Using these cell-free systems, even non-coded (unnatural) amino acids can be
incorporated into the proteins to expand the chemical repertoire of proteins which may be required for more stability or
novel function for the industrial application of designed proteins (Noren, Anthony-Cahill, Griffith, & Schultz, 1989).
1.6
Omics technologies
The term omics refers to a scientific discipline in biology that ends with “omics.” With the recent advancement in technologies the suffix “omics” has been applied to techniques that assess some features of a vast family of biological molecules, such as the genome, proteome, transcriptome, or metabolome, respectively (Fig. 1.3; Vailati-Riboni, Palombo, &
Loor, 2017). Omics studies are also referring to a group of technologies that are used to investigate the roles, connections, and activities of the many types of molecules that make up a complete cell of an organism. The comprehensive
capabilities of omics technologies in the context of the cell, tissue, or organism is its distinguishing feature. They are
primarily intended for the non-targeted and non-biased identification of genes, mRNAs, proteins, and metabolites in a
biological sample. Each of these areas opens us previously unimaginable possibilities for understanding the physiological state and cellular activities in the cell (Roh, Nerem, & Roy, 2016; Zhang, Sun, & Ma, 2017). High-throughput methods can quickly offer a comprehensive understanding of biological activities at various levels, enabling faster resolving
the challenges in health, disease detection, and cellular metabolism as well as uncover gaps in present understanding
(Baidoo & Benites, 2019). Moreover, in situations where no hypothesis is known or mandated owing to a lack of data,
the omics approach is appropriate for hypothesis-generating studies, as holistic techniques gather and evaluate all relevant data to establish a hypothesis, which can then be tested (Roberts, Souza, Gerszten, & Clish, 2012). The automated
DNA protein sequencer and ink-jet DNA synthesizer, developed by Leroy Hood and colleagues in the early 1990s for
gene expression analysis, were the first omics technologies (Hood, 2002). They also developed a protein sequencer and
synthesizer for protein expression at the cellular level. Furthermore, the development of metabolomics investigations,
which were initiated by Oliver, Winson, Kell, and Baganz (1998) completed the physiological cycle of biological information processing and synthesis, from gene expression through protein synthesis to metabolite alterations.
1.6.1 Genomics and transcriptomics
Genomics refers to the study of the complete genome of an organism. Whereas transcriptomic refers to the study of the
entire RNA (i.e., mRNA, non-coding RNA, rRNA, snRNA, and tRNA) expressed by a cell, therefore representing a
FIGURE 1.3 Different branches of
omics technology.
10
PART | A Modern technological advancements in the field of animal experimentation
snapshot of cellular metabolism (Pagani et al., 2012). Synthetic biology generally relies on the creation of predictable and
favored phenotypes, thus tight and adjustable regulation of gene expression is extremely desirable. The gathering of
genome-scale data has never been simpler. However, the advancement in sequencing technologies has expanded our capacity to study and interpret the entire genomes of organisms using high throughput sequencing technology. It will also be narrowing the gap between genotype and phenotype. Genome-wide association studies have become the standard approach
for identifying candidate areas associated with complex characteristics of interest in humans and other species since the
advent of genomics (Gondro, Van Der Werf, & Hayes, 2013). Probe-based chips with a high number of SNP markers distributed over the genome (Capomaccio, Milanesi, Bomba, Vajana, & Ajmone-Marsan, 2015; Vailati-Riboni et al., 2017).
Haemophilus influenzae is the first organism to sequence a complete genome (Fleischmann et al., 1995). Since then,
numerous genomes have been sequenced including the human genome, and used for understanding the evolution of organisms, mutations in DNA, prediction of newly emerging infectious diseases, etc. (Fricke, Rasko, & Ravel, 2009).
Transcriptome means all the mRNA while proteome refers to all the proteins expressed in an organism. The transcriptomic era begins when Schena, Shalon, Davis, and Brown (1995) for the first time created the “micro-array” technology. It allowed for the large-scale study of a specified set (from hundreds to thousands) of cellular mRNA using an
ink-jet DNA synthesizer. Transcriptomics has been transformed by the recent advancement of high-throughput nextgeneration sequencing (NGS) technology, which allows RNA analysis using massively parallel cDNA sequencing
(RNAseq; Voelkerding, Dames, & Durtschi, 2009). The restricted dynamic range of detection, which was a problem
with microarray technologies, was no longer an issue with this technique.
1.6.2 Proteomics
Proteomics is the study of proteins and peptides generated by cells at various phases of their development and life
cycle, as well as in biological systems under specific growth conditions. Proteomics may also be used to investigate the
temporal dynamics of protein expression or posttranslational modification (PTM; Catherman, Skinner, & Kelleher,
2014; VerBerkmoes, Denef, Hettich, & Banfield, 2009). A proteomic analysis, on the other hand, gives the protein
inventory of a cell or tissue at a certain time point, allowing for the discovery of new biomarkers, the identification and
localization of PTMs, and the investigation of protein-protein interactions (Chandramouli & Qian, 2009). In fact, all the
applications of genetic engineering are possible only because of genome sequencing. Proteins are the effector molecules
of almost all the biological functions in the body. Proteomics deals with the study of the structure and function of proteins including their interactions and modifications (Nesvizhskii & Aebersold, 2005; Wolters, Washburn, & Yates,
2001). Proteomic methods have been developed to detect and differentially measure protein species in complicated biological data, and cattle researchers are using them (Lippolis & Reinhardt, 2008; Sauerwein, Bendixen, Restelli, &
Ceciliani, 2014). Mass spectrometry (Ms) is a method in which all chemical components in a sample are ionized and
the resultant charged molecules (ions) are evaluated according to their mass-to-charge (m/z) ratios, is at the core of current proteomics (Aebersold & Mann, 2003; Marx, 2013). One- or two-dimensional polyacrylamide gel electrophoresis
(1D-PAGE, 2D-PAGE) is commonly employed for a simple pre-separation of complicated protein mixtures before Ms
analysis. However, other forms of liquid chromatography (LC or HPLC) are utilized to supplement or replace gel-based
separation techniques to further automate the process and build a streaming pipeline analysis (Bian et al., 2020; Krisp,
Yang, Van Soest, & Molloy, 2015; Wilson, Vehus, Berg, & Lundanes, 2015).
The field of proteomics has numerous applications like Disease diagnosis, identification of potential vaccine candidates, food microbiology, etc. (Catherman et al., 2014). Proteomics can comprehensively study the difference in the
proteome of various tissues and disease conditions (Rühl et al., 2019). The key technology in proteomics is a mass spectrometer which can determine the mass of a lot of proteins at the same time and with 1 Dalton precision. Traditionally
the proteins were identified by gel electrophoresis and western blotting but various forms have not only decreased the
amount of biological sample required but also decreased cost, time. Through mass spectroscopy, we can also detect
even PTMs of a protein in a cell (Kleiner, 2019; Yates, 2019).
1.6.3 Metabolomics
The global profiling of metabolites in a biological sample is known as the metabolome. The term metabolome is concerned with the low-weight molecules called metabolites present in a cell or tissue or a sample. A metabolomics analysis
may be performed on a range of biological fluids and tissue types, and it can be done using a variety of technological
platforms. To provide an integrated picture of the metabolome, metabolomics generally combines high-resolution analysis with statistical methods such as principal component analysis and partial least squares (Zhang et al., 2012). NMR is
Emerging techniques in biological sciences Chapter | 1
11
FIGURE 1.4 Flow diagram of multiple data sources in a multiomics framework.
one of the most widely used spectroscopic analytical methods for identifying and quantifying a wide range of organic
substances in the micro-molar range, providing unbiased information about metabolite profiles. Peptides, amino acids,
nucleic acids, carbohydrates, organic acids, vitamins, polyphenols, alkaloids, and inorganic species are among the compounds that may be detected with this method. In high-throughput metabolomics, Ms is increasingly being used in conjunction with other methods like chromatography or electrophoretic approaches. Ms has been the technique of choice in
many metabolomic investigations because of its excellent sensitivity and the large range of covered compounds (Zhang
et al., 2012). Mass spectroscopy along with various chromatographic separation techniques has also made it possible to
study the complete metabolic profile, lipid profile, and carbohydrates, which are respectively called metabolomics and
lipidomics and glycomics. Metabolomics is an excellent tool for the molecular phenotyping of an organism and thus
can suggest how patients will respond to particular drugs or how severe disease can be for a particular patient depending upon his metabolome. The era of single omics is over now as today is the age of ‘multi-omics, the integrative
approach. Using multi-omics approaches can lead to better disease diagnosis, prognosis, and treatment. The multiomic
technology combined with the system biology approach and artificial intelligence can help in generating a patienttailored health care approach specific for every individual depending upon his/her genome, transcriptome, proteome,
and metabolome (Fig. 1.4).
1.7
Informatics and simulations
Various fields of science have been transformed by increasing computing power and information technology. In biological
sciences it helps in designing complex experiments, narrowing down possibilities, and sometimes even deciphering information about processes that are not accessible through any other technique yet. For instance, simulation of protein folding
has given valuable insights into the mechanism of protein folding, and combined with artificial intelligence, it can predict
the structure of proteins. The experimental techniques are not adequate to probe the detailed mechanism of protein folding
which is very fast (few microseconds to milliseconds). The folded structure of some proteins is also not feasible to determine experimentally, specifically for membrane proteins. Protein structure prediction has been one of the most important
scientific challenges because the structure of a protein opens the path to understand its function, mechanism. One application of this is the drug target prediction and how they are likely to interact with the drugs. The best methods of protein
structure prediction are announced every two years through a competition called CASP (Critical Assessment of Structure
Prediction). The artificial intelligence system called alpha fold, housed at Google Inc. subsidiary DeepMind, has performed the best in CASP14 held in 2020 (Alam & Shehu, 2021). But these knowledge-based methods performing the best
in CASP competitions so far are only up to 90% accurate. These methods are based on the experimentally known structure
of a similar sequence of proteins. The other approach is de novo structure prediction which is based on interatomic forces
12
PART | A Modern technological advancements in the field of animal experimentation
FIGURE 1.5 Integration of omics technology, systems biology and artificial
Intelligence may help developed personalized healthcare systems.
that folds the protein. But given the so many atoms of the protein molecule itself and the surrounding solvent, such calculations are too much for the presently available commuting power in most of the labs. The approximation of forces
accounted for interatomic interactions is also not perfect. But in the future, more computing power and better actuation of
forces involved are expected to determine the structures of new protein sequences by de novo methods. Although the
structure prediction of new natural protein sequences remains uncertain, the state of art has advanced reasonably to design
proteins like therapeutic antibodies, enzymes, etc. Michael Levitt had developed an algorithm for humanizing antibodies
that can transplant the binding site from animal antibodies to the scaffold of human antibodies (Queen et al., 1989).
Regarding the simulation studies in general, Albert Einstein has said, “everything that counts is not counted and everything that is counted, does not count.” Therefore, the ultimate proof of the structure or any truth is experimentation but
simulations help in narrowing down possibilities. Such narrowing down of possibilities in other experiments also can be
of big help. For example, in-silico drug design can narrow down the possibility to a few molecules whereas testing molecules randomly will waste a lot of resources as well as kill a lot of animals in experimentation (Gray, Sidhu,
Chandrasekera, Hendriksen, & Borrebaeck, 2016). Nowadays the simulation in biological sciences is used at various levels
of detail according to the scale of the system in simulation. Quantum calculations can be performed for a small drug molecule but for the larger systems, the forces have to be accounted for in a coarse-grained manner because the computing
power available is limited. The systems of every level in biology are studied using simulations like, the structure of drug
molecules, cellular processes, cell-cell interaction, the functioning of organs, and up to the whole organism (Fig. 1.5).
Computational science is crucial for the study of biological networks at the cellular and system level.
The massive data accumulating about biological systems and increasing the information storage capacity of computers
is bringing in BigData analytics in biological sciences. The number of databases about various biological sciences is
increasing. The Nucleic Acid Research journal maintains a comprehensive catalog of biological informatics resources at its
website: https://www.oxfordjournals.org/nar/database/c/ This includes databases about research literature, protein structure,
genomes of various organisms, secondary databases, up to the ecological and biodiversity databases (Rigden & Fernández,
2021). A comprehensive analysis of such massive information is possible through computational methods/algorithms only.
The development of such algorithms and data structures is now a separate discipline within biological sciences called bioinformatics. Research in life sciences today is unlikely to be well-informed research without the use of bioinformatics.
1.8
Automation and miniaturization of experiments
Microfluidics is basically the miniaturizing of the lab experiments at very small scale in the form of a chip hence called
“lab on chip.” It involves the manipulation of fluids in small sized (in microns) channels. The miniaturization of
Emerging techniques in biological sciences Chapter | 1
13
FIGURE 1.6 Application of microfluidics is various fields of biology.
FIGURE 1.7 Diagram illustrating fluorescence in situ hybridization (FISH) on microfluidic chip.
experimental setups through microfluidics has not only decreased the long-run cost of experiments but also enables the
study of new forces and phenomena affecting systems at the micro and nanoscale (Kohl et al., 2021). Microfluidics
requires less volume of chemicals (in pico to nanoliters) and the portability of setup makes it possible to design point of
care devices (Dabbagh et al., 2021). Due to less volume of consumables required the cost is less therefore high throughput studies can be done at an affordable cost. These advanced setups are also used to understand the forces at nano and
micro scale which is called nano-science (Fig. 1.6).
Sometimes the unexpected phenomena observed at the macro scale of a system are caused by phenomena at smaller
scales such as quantum phenomena. Microfluidics is particularly helpful in such cases as it can be used to study the
mechanisms at nano scale level. All these features have enabled the biologists to use microfluidics in various fields
such as Next Generation DNA sequencing, Polymerase Chain Reaction (PCR), Protein crystallization etc. In microfluidics, reaction volumes are in nano scale, similar to those typically found in living cells which makes the in vitro studies
more accurate (Fig. 1.7).
So, in recent years, microfluidics is used in drug discovery, chemical synthesis, screening of compounds and preclinical testing of drugs on living cells. Automation of repetitive experimental work using robotics has not only made
experimentation easy but also more accurate, precise, and free of human errors (Tegally, San, Giandhari, & de Oliveira,
2020). Automated liquid handling systems are commercially available for proteomics, transcriptomics, and genomics
research as well as for diagnostic as commercial scale. This had been feasible due to reducing the cost of electronic circuit chips (Gach, Iwai, Kim, Hillson, & Singh, 2017; Table 1.2).
14
PART | A Modern technological advancements in the field of animal experimentation
TABLE 1.2 Emerging techniques in life sciences.
Techniques
Applications
Principle
Instrument infrastructure
Informatics and
simulation
Data analysis, prediction
of structure, function, and
intervention design
Mathematics and statistics
Supercomputing facility, data
Next-generation
sequencing
Genome sequence, gene
expression
Optics, electronics,
nanoscience
Optoelectronic fabrication
Genetic
engineering
(CRISPR, RNAi)
Crop improvement,
therapeutics
Molecular biology and
genetics
Reagents, oligonucleotide synthesis and
protein purification facility
Omics technologies
transcriptomics,
proteomics,
metabolomics
System biology
Electrophoresis, separation of
ions by their mass-to-charge
ratio (m/z), oligonucleotide
hybridization
Mass spectrometer robotic liquid handler
Spectroscopy
Molecular structure
determination and
chemical composition of
mixtures or systems
Nuclear magnetic resonance
(NMR), light diffraction,
absorbance, optics
NMR spectrometer cyclotron, synchrotron
for a collimated light source
Imaging
Structure of molecules to
cells and organs
Optics, electronics
Electron microscope, imaging facility
BioMEMS
Diagnosis, biology at
micro and nanoscale
details
Electronics, optics fluidics,
nanoscience
Cleanroom facilities and micro-nano
fabrication facility for microfluidic systems
with optical, electrical, and mechanical
systems integrated
1.9
BioMEMS (Biomedical micro electro-mechanical systems in biology)
Microelectromechanical systems (Bio-MEMS) are being developed with more and more functionalities by integrating
the optics, electronics, and mechanical systems in microfluidic channels (Ino et al., 2020). These BioMEMS can do precise manipulation of biosystems and also closely observe the behavior/response. There emerge some new phenomena
and forces at these nanoscale dimensions such as surface plasmon response which is the interaction of electromagnetic
waves with electrons. Similarly, such forces at work in biological systems can also be studied through such miniaturized
experimental setups and detection schemes integrated into it. These miniaturized systems are also ultimately less costly
for large-scale experiments like high throughput assays in research and diagnosis. Some of these devices like LFA
(Immunochromatographic lateral flow assays) and glucometer have come up as point-of-care kits for diagnosis.(Huang
et al., 2020). Microfluidics has made it possible to create a microenvironment around cells that is similar to the environment they experience in tissue or organs. Various functionalities are integrated into microfluidic channels for 3D cell
culture and an organ-on-chip (Thompson, Fu, Knight, & Thorpe, 2020). The use of these organs-on-chips not only does
away with ethical clearance required in case of animal experiments but they are also less time consuming and more
controlled experiment, enabling a clear understanding of cause-and-effect relationship. The origin of these technologies
can be attributed to flow cytometry wherein the fluid flow is controlled to line up the cells and count them using
advanced optics. Quake et al. had proposed the idea of microfluidic cell sorters (Fu, Chou, Spence, Arnold, & Quake,
2002). If a reasonable purity of sorted cells is achieved then they can be very valuable for label-free sorting of sperms
for gender selection in cattle, separating out cancerous cells from blood, harvesting stem cells, etc. (Fig. 1.8).
Microfluidic systems are designed to automate the process of DNA profiling from minute forensic samples such as
a touch of a finger (Woolf et al., 2020). These devices extract the DNA, PCR amplifies the short tandem repeat (STRs),
and detect the size of amplified STRs to create the DNA profile (Hong et al., 2020). These all-in-one DNA profiling
devices are used on-site such as police stations and mobile forensic labs. Latest developments in next-generation DNA
sequencing like oxford nanopore technologies are a cutting-edge example of integrating molecular machines, microfluidics, and sophisticated sensors (Kumar, Cowley, & Davis, 2019). They use protein as nanopores through which the
DNA is ratcheted by enzymes. This process is inspired by the processive enzymes in cells that work on DNA (Patel
Emerging techniques in biological sciences Chapter | 1
15
FIGURE 1.8 Glucometer as an example of BioMEMS.
et al., 2018). Then the integrated optics and electronics are used to sense the chemical identity of nucleotides passing
through the nanopore to ultimately determine the sequence of the whole genome. This oxford nanopore NGS is very
error-prone but future developments are expected to make these devices accurate and so cost-effective that almost all
can get their genome sequenced for use in personalized medicine (Cohen, 2020).
Many new techniques have emerged at the interface of various disciplines like electronics, optics, molecular biology, genetics, computational science, and nanoscience. The majority of them require expensive instruments and
manufacturing facilities therefore they are widely accessible. Recently the scientific community is also emphasizing
techniques that are high tech but low cost so that the benefits of science can reach common people. It is about democratizing science by involving more people in science, keeping common people in view as the purpose of science, and
making it accessible for common people to experience science. In this spirit, educational kits are made to demonstrate
scientific concepts and invoke scientific temperament, data, and ideas that are crowd-sourced in scientific projects; and
the development of low cost, as well as point of care scientific solutions, are also being given attention.
References
Aebersold, R., & Mann, M. (2003). Mass spectrometry-based proteomics. Nature, 422(6928), 198 207. Available from https://doi.org/10.1038/
nature01511.
Alam, F. F., & Shehu, A. (2021). Unsupervised multi-instance learning for protein structure determination. Journal of Bioinformatics and
Computational Biology, 2140002. Available from https://doi.org/10.1142/S0219720021400023.
Axelrod, D. (2001). Total internal reflection fluorescence microscopy in cell biology. Traffic (Copenhagen, Denmark), 2(11), 764 774.
Baidoo, E. E., & Benites, V. T. (2019). Mass spectrometry-based microbial metabolomics: Techniques, analysis, and applications. Microbial metabolomics (pp. 11 69). New York: Humana Press.
Bao, A., Chen, H., Chen, L., Chen, S., Hao, Q., Guo, W., . . . Cao, D. (2019). ). CRISPR/Cas9-mediated targeted mutagenesis of GmSPL9 genes alters
plant architecture in soybean. BMC Plant Biology, 19(1), 1 12.
Bian, Y., Zheng, R., Bayer, F. P., Wong, C., Chang, Y. C., Meng, C., . . . Kuster, B. (2020). Robust, reproducible and quantitative analysis of thousands of proteomes by micro-flow LC MS/MS. Nature Communications, 11(1), 1 12.
Böhmer, M., & Enderlein, J. (2003). Orientation imaging of single molecules by wide-field epifluorescence microscopy. JOSA B, 20(3), 554 559.
Cai, L., Fisher, A. L., Huang, H., & Xie, Z. (2016). CRISPR-mediated genome editing and human diseases. Genes & Diseases, 3(4), 244 251.
Available from https://doi.org/10.1016/j.gendis.2016.07.003.
Calderaro, J., & Kather, J. N. (2021). Artificial intelligence-based pathology for gastrointestinal and hepatobiliary cancers. Gut, 70(6), 1183 1193.
Campbell, T. N., & Choy, F. Y. (2005). RNA interference: Past, present and future. Current Issues in Molecular Biology, 7(1), 1 6.
Capomaccio, S., Milanesi, M., Bomba, L., Vajana, E., & Ajmone-Marsan, P. (2015). MUGBAS: A species free gene-based programme suite for postGWAS analysis. Bioinformatics (Oxford, England), 31(14), 2380 2381.
Catherman, A. D., Skinner, O. S., & Kelleher, N. L. (2014). Top down proteomics: Facts and perspectives. Biochemical and Biophysical Research
Communications, 445(4), 683 693.
16
PART | A Modern technological advancements in the field of animal experimentation
Champer, J., Buchman, A., & Akbari, O. S. (2016). Cheating evolution: Engineering gene drives to manipulate the fate of wild populations. Nature
Reviews Genetics, 17(3), 146 159.
Chandramouli, K., & Qian, P. Y. (2009). Proteomics: Challenges, techniques and possibilities to overcome biological sample complexity. Human
Genomics and Proteomics: HGP, 2009.
Chen, B., Zou, W., Xu, H., Liang, Y., & Huang, B. (2018). Efficient labeling and imaging of protein-coding genes in living cells using CRISPR-Tag.
Nature Communications, 9(1), 1 9.
Chen, L., Wang, R. S., & Zhang, X. S. (2009). Biomolecular networks: Methods and applications in systems biology (Vol. 10). John Wiley & Sons.
Cohen, J. (2020). CRISPR, the revolutionary genetic ‘scissors,’ honored by Chemistry Nobel. Science (New York, N.Y.). Available from https://doi.
org/10.1126/science.abf0540.
Cong, L., Ran, F. A., Cox, D., Lin, S., Barretto, R., Habib, N., . . . Zhang, F. (2013). Multiplex genome engineering using CRISPR/Cas systems.
Science (New York, N.Y.), 339(6121), 819 823.
Dabbagh, S. R., Becher, E., Ghaderinezhad, F., Havlucu, H., Ozcan, O., Ozkan, M., . . . Tasoglu, S. (2021). Increasing the packing density of assays in
paper-based microfluidic devices. Biomicrofluidics, 15(1), 011502. Available from https://doi.org/10.1063/5.0042816.
Dubochet, J., Adrian, M., Chang, J. J., Homo, J. C., Lepault, J., McDowall, A. W., . . . Schultz, P. (1988). Cryo-electron microscopy of vitrified specimens. Quarterly Reviews of Biophysics, 21(2), 129 228.
Edmondson, R., Broglie, J. J., Adcock, A. F., & Yang, L. (2014). Three-dimensional cell culture systems and their applications in drug discovery and
cell-based biosensors. Assay and Drug Development Technologies, 12(4), 207 218.
El Achi, H., & Khoury, J. D. (2020). Artificial intelligence and digital microscopy applications in diagnostic hematopathology. Cancers, 12(4), 797.
El-Mounadi, K., Morales-Floriano, M. L., & Garcia-Ruiz, H. (2020). Principles, applications, and biosafety of plant genome editing using CRISPRCas9. Frontiers in plant science, 11, 56.
Fish, K. N. (2009). Total internal reflection fluorescence (TIRF) microscopy. Current Protocols in Cytometry, 50(1), 12 18.
Fleischmann, R. D., Adams, M. D., White, O., Clayton, R. A., Kirkness, E. F., Kerlavage, A. R., . . . Merrick, J. M. (1995). Whole-genome random
sequencing and assembly of Haemophilus influenzae Rd. Science (New York, N.Y.), 269(5223), 496 512.
Fricke, W. F., Rasko, D. A., & Ravel, J. (2009). The role of genomics in the identification, prediction, and prevention of biological threats. PLoS
Biology, 7(10), e1000217.
Fu, A. Y., Chou, H.-P., Spence, C., Arnold, F. H., & Quake, S. R. (2002). An integrated microfabricated cell sorter. Analytical Chemistry, 74(11),
2451 2457. Available from https://doi.org/10.1021/ac0255330.
Gach, P. C., Iwai, K., Kim, P. W., Hillson, N. J., & Singh, A. K. (2017). Droplet microfluidics for synthetic biology. Lab on a Chip, 17(20),
3388 3400. Available from https://doi.org/10.1039/c7lc00576h.
Gest, H. (2009). Homage to Robert Hooke (1635 1703): New insights from the recently discovered Hooke Folio. Perspectives in Biology and
Medicine, 52(3), 392 399.
Gondro, C., Van Der Werf, J. H., & Hayes, B. (2013). Genome-wide association studies and genomic prediction. Humana Press.
Gray, A. C., Sidhu, S. S., Chandrasekera, P. C., Hendriksen, C. F. M., & Borrebaeck, C. A. K. (2016). Animal-friendly affinity reagents: Replacing
the needless in the haystack. Trends in Biotechnology, 34(12), 960 969. Available from https://doi.org/10.1016/j.tibtech.2016.05.017.
Hara, M., Morishige, N., Chikama, T. I., & Nishida, T. (2003). Comparison of confocal biomicroscopy and noncontact specular microscopy for evaluation of the corneal endothelium. Cornea, 22(6), 512 515.
Hastings, C. (2021). Available from https://www.medgadget.com/2021/06/microarray-rapidly-identifies-antibodies-against-sars-cov-2.html
Hong, Y., Singh, N., Bamopoulos, S., Gjerga, E., Schmalbrock, L. K., Balabanian, K., . . . Wirth, M. (2020). Authentication of primary murine cell
lines by a microfluidics-based lab-on-chip system. Biomedicines, 8(12). Available from https://doi.org/10.3390/biomedicines8120590.
Hood, L. (2002). A personal view of molecular technology and how it has changed biology. Journal of Proteome Research, 1(5), 399 409.
Hoover, E. E., & Squier, J. A. (2013). Advances in multiphoton microscopy technology. Nature Photonics, 7(2), 93 101.
Hu, Q., Maurais, E. G., & Ly, P. (2020). Cellular and genomic approaches for exploring structural chromosomal rearrangements. Chromosome
Research, 28(1), 19 30.
Huang, L., Tian, S., Zhao, W., Liu, K., Ma, X., & Guo, J. (2020). Multiplexed detection of biomarkers in lateral-flow immunoassays. The Analyst,
145(8), 2828 2840. Available from https://doi.org/10.1039/c9an02485a.
Ino, K., Ozawa, F., Dang, N., Hiramoto, K., Hino, S., Akasaka, R., . . . Shiku, H. (2020). Biofabrication using electrochemical devices and systems.
Advanced Bio Systems, 4(4), e1900234. Available from https://doi.org/10.1002/adbi.201900234.
Jayavelu, N. D. (2015). Integrative systems biology approaches for analyzing high-throughput data applications to modeling of gene regulatory networks. Cas9. Frontiers in plant science, 11, 56.
Kleiner, M. (2019). Metaproteomics: Much more than measuring gene expression in microbial communities. Msystems, 4(3), e00115 e00119.
Kohl, Y., Biehl, M., Spring, S., Hesler, M., Ogourtsov, V., Todorovic, M., . . . Knoll, T. (2021). Microfluidic in vitro platform for (nano)safety and
(nano)drug efficiency screening. Small (Weinheim an Der Bergstrasse, Germany), e2006012. Available from https://doi.org/10.1002/
smll.202006012.
Köhler, A. (1894). New method of illumination for photomicrographical purposes. Journal of the Royal Microscopical Society, 14, 261 262.
Konwarh, R. (2020). Can CRISPR/Cas technology be a felicitous stratagem against the COVID-19 fiasco? Prospects and hitches. Frontiers in
Molecular Biosciences, 7, 557377. Available from https://doi.org/10.3389/fmolb.2020.557377.
Krisp, C., Yang, H., Van Soest, R., & Molloy, M. P. (2015). Online peptide fractionation using a multiphasic microfluidic liquid chromatography chip
improves reproducibility and detection limits for quantitation in discovery and targeted proteomics*[S]. Molecular & Cellular Proteomics, 14(6),
1708 1719.
Emerging techniques in biological sciences Chapter | 1
17
Kumar, K. R., Cowley, M. J., & Davis, R. L. (2019). Next-generation sequencing and emerging technologies. Seminars in Thrombosis and
Hemostasis, 45(7), 661 673. Available from https://doi.org/10.1055/s-0039-1688446.
Kumar, S., Srinivasan, A., & Nikolajeff, F. (2018). Role of infrared spectroscopy and imaging in cancer diagnosis. Current Medicinal Chemistry, 25
(9), 1055 1072. Available from https://doi.org/10.2174/0929867324666170523121314.
Lippolis, J. D., & Reinhardt, T. A. (2008). Centennial paper: proteomics in animal science. Journal of Animal Science, 86(9), 2430 2441.
Liu, T. L., Upadhyayula, S., Milkie, D. E., Singh, V., Wang, K., Swinburne, I. A., . . . Betzig, E. (2018). Observing the cell in its native state: Imaging
subcellular dynamics in multicellular organisms. Science (New York, N.Y.), 360(6386).
Mamoshina, P., Vieira, A., Putin, E., & Zhavoronkov, A. (2016). Applications of deep learning in biomedicine. Molecular Pharmaceutics, 13(5),
1445 1454.
Marx, V. (2013). Targeted proteomics. Nature Methods, 10(1), 19.
Nasri, M., Mir, P., Dannenmann, B., Amend, D., Skroblyn, T., Xu, Y., . . . Skokowa, J. (2019). Fluorescent labeling of CRISPR/Cas9 RNP for gene
knockout in HSPCs and iPSCs reveals an essential role for GADD45b in stress response. Blood Advances, 3(1), 63 71.
Nesvizhskii, A. I., & Aebersold, R. (2005). Interpretation of shotgun proteomic data. Molecular & Cellular Proteomics, 4(10), 1419 1440.
Nomarski, G., & Weill, A. R. (1955). Application to metallography of interference methods with two polarized waves. Journal of Metallurgy, 52(2),
121 134.
Noren, C. J., Anthony-Cahill, S. J., Griffith, M. C., & Schultz, P. G. (1989). A general method for site-specific incorporation of unnatural amino acids
into proteins. Science (New York, N.Y.), 244(4901), 182 188. Available from https://doi.org/10.1126/science.2649980.
Oliver, S. G., Winson, M. K., Kell, D. B., & Baganz, F. (1998). Systematic functional analysis of the yeast genome. Trends in Biotechnology, 16(9),
373 378.
Pagani, I., Liolios, K., Jansson, J., Chen, I. M. A., Smirnova, T., Nosrat, B., . . . Kyrpides, N. C. (2012). The Genomes OnLine Database (GOLD) v. 4:
Status of genomic and metagenomic projects and their associated metadata. Nucleic Acids Research, 40(D1), D571 D579.
Pare, J. M., & Hobman, T. C. (2007). Dicer: Structure, function and role in RNA-dependent gene-silencing pathways. Industrial enzymes
(pp. 421 438). Dordrecht: Springer.
Park, S. H., & Bao, G. (2021). CRISPR/Cas9 gene editing for curing sickle cell disease. Transfusion and Apheresis Science: Official Journal of the
World Apheresis Association: Official Journal of the European Society for Haemapheresis, 103060. Available from https://doi.org/10.1016/j.
transci.2021.103060.
Patel, A., Belykh, E., Miller, E. J., George, L. L., Martirosyan, N. L., Byvaltsev, V. A., . . . Preul, M. C. (2018). MinION rapid sequencing: Review of
potential applications in neurosurgery. Surgical Neurology International, 9, 157. Available from https://doi.org/10.4103/sni.sni_55_18.
Puchta, H. (2005). The repair of double-strand breaks in plants: Mechanisms and consequences for genome evolution. Journal of Experimental
Botany, 56(409), 1 14.
Puchta, H., Dujon, B., & Hohn, B. (1996). Two different but related mechanisms are used in plants for the repair of genomic double-strand breaks by
homologous recombination. Proceedings of the National Academy of Sciences, 93(10), 5055 5060.
Queen, C., Schneider, W. P., Selick, H. E., Payne, P. W., Landolfi, N. F., Duncan, J. F., . . . Waldmann, T. A. (1989). A humanized antibody that binds
to the interleukin 2 receptor. Proceedings of the National Academy of Sciences, 86(24), 10029 10033. Available from https://doi.org/10.1073/
pnas.86.24.10029.
Quétier, F. (2016). The CRISPR-Cas9 technology: Closer to the ultimate toolkit for targeted genome editing. Plant Science, 242, 65 76.
Renz, M. (2013). Fluorescence microscopy—A historical and technical perspective. Cytometry Part A, 83(9), 767 779.
Rigden, D. J., & Fernández, X. M. (2021). The 2021 Nucleic Acids Research database issue and the online molecular biology database collection.
Nucleic Acids Research, 49(D1), D1 D9. Available from https://doi.org/10.1093/nar/gkaa1216.
Roberts, L. D., Souza, A. L., Gerszten, R. E., & Clish, C. B. (2012). Targeted metabolomics. In M. Frederick, Ausubel, et al. (Eds.), Current protocols
in molecular biology (1, pp. 1 24). . Available from http://doi.org/10.1002/0471142727.mb3002s98.
Roh, K. H., Nerem, R. M., & Roy, K. (2016). Biomanufacturing of therapeutic cells: State of the art, current challenges, and future perspectives.
Annual Review of Chemical and Biomolecular Engineering, 7, 455 478.
Rollin, J. A., Tam, T. K., & Zhang, Y.-H. P. (2013). New biotechnology paradigm: Cell-free biosystems for biomanufacturing. Green Chemistry, 15
(7), 1708. Available from https://doi.org/10.1039/c3gc40625c.
Rost, F. W. (1992). Fluorescence microscopy (Vol. 2). Cambridge University Press.
Rühl, M., Kühn, B., Roos, J., Maier, T. J., Steinhilber, D., & Karas, M. (2019). Elucidation of chemical modifier reactivity towards peptides and proteins and the analysis of specific fragmentation by matrix-assisted laser desorption/ionization collision-induced dissociation tandem mass spectrometry. Rapid Communications in Mass Spectrometry, 33, 40 49.
Sandler, R. (2020). The ethics of genetic engineering and gene drives in conservation. Conservation Biology, 34(2), 378 385.
Sauerwein, H., Bendixen, E., Restelli, L., & Ceciliani, F. (2014). The adipose tissue in farm animals: A proteomic approach. Current Protein and
Peptide Science, 15(2), 146 155.
Saurabh, S., Vidyarthi, A. S., & Prasad, D. (2014). RNA interference: Concept to reality in crop improvement. Planta, 239(3), 543 564. Available
from https://doi.org/10.1007/s00425-013-2019-5.
Schena, M., Shalon, D., Davis, R. W., & Brown, P. O. (1995). Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science (New York, N.Y.), 270(5235), 467 470.
Schiml, S., & Puchta, H. (2016). Revolutionizing plant biology: Multiple ways of genome engineering by CRISPR/Cas. Plant Methods, 12(1), 1 9.
Sontheimer, E. J. (2005). Assembly and function of RNA silencing complexes. Nature Reviews. Molecular Cell Biology, 6(2), 127 138.
18
PART | A Modern technological advancements in the field of animal experimentation
Symington, L. S., & Gautier, J. (2011). Double-strand break end resection and repair pathway choice. Annual Review of Genetics, 45, 247 271.
Takahashi, K., & Yamanaka, S. (2006). Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors.
Cell, 126(4), 663 676. Available from https://doi.org/10.1016/j.cell.2006.07.024.
Tegally, H., San, J. E., Giandhari, J., & de Oliveira, T. (2020). Unlocking the efficiency of genomics laboratories with robotic liquid-handling. BMC
Genomics, 21(1), 729. Available from https://doi.org/10.1186/s12864-020-07137-1.
Thompson, C. L., Fu, S., Knight, M. M., & Thorpe, S. D. (2020). Mechanical stimulation: A crucial element of organ-on-chip models. Frontiers in
Bioengineering and Biotechnology, 8, 602646. Available from https://doi.org/10.3389/fbioe.2020.602646.
Vailati-Riboni, M., Palombo, V., & Loor, J. J. (2017). What are omics sciences? Periparturient diseases of dairy cows (pp. 1 7). Cham: Springer.
Valenti, M. T., Serena, M., Carbonare, L. D., & Zipeto, D. (2019). CRISPR/Cas system: An emerging technology in stem cell research. World
Journal of Stem Cells, 11(11), 937 956. Available from https://doi.org/10.4252/wjsc.v11.i11.937.
VerBerkmoes, N. C., Denef, V. J., Hettich, R. L., & Banfield, J. F. (2009). Functional analysis of natural microbial consortia using community proteomics. Nature Reviews Microbiology, 7(3), 196 205.
Voelkerding, K. V., Dames, S. A., & Durtschi, J. D. (2009). Next-generation sequencing: From basic research to diagnostics. Clinical Chemistry, 55
(4), 641 658.
Wang, B., Yang, H., Sun, J., Dou, C., Huang, J., & Guo, F.-B. (2021). BioMaster: An integrated database and analytic platform to provide comprehensive information about biobrick parts. Frontiers in Microbiology, 12, 593979. Available from https://doi.org/10.3389/fmicb.2021.593979.
Wilson, S. R., Vehus, T., Berg, H. S., & Lundanes, E. (2015). Nano-LC in proteomics: Recent advances and approaches. Bioanalysis, 7(14),
1799 1815.
Wollman, A. J., Nudd, R., Hedlund, E. G., & Leake, M. C. (2015). From Animaculum to single molecules: 300 years of the light microscope. Open
Biology, 5(4), 150019.
Wolters, D. A., Washburn, M. P., & Yates, J. R. (2001). An automated multidimensional protein identification technology for shotgun proteomics.
Analytical Chemistry, 73(23), 5683 5690.
Woolf, M. S., Dignan, L. M., Lewis, H. M., Tomley, C. J., Nauman, A. Q., & Landers, J. P. (2020). Optically-controlled closable microvalves for
polymeric centrifugal microfluidic devices. Lab on a Chip, 20(8), 1426 1440. Available from https://doi.org/10.1039/C9LC01187K.
Yates, J. R., III (2019). Recent technical advances in proteomics. F1000Research, 8.
Zernike, F. (1955). How I discovered phase contrast. Science (New York, N.Y.), 121(3141), 345 349.
Zhang, J., Minary, P., & Levitt, M. (2012). Multiscale natural moves refine macromolecules using single-particle electron microscopy projection
images. Proceedings of the National Academy of Sciences, 109(25), 9845 9850. Available from https://doi.org/10.1073/pnas.1205945109.
Zhang, Y. H. P., Sun, J., & Ma, Y. (2017). Biomanufacturing: History and perspective. Journal of Industrial Microbiology and Biotechnology, 44
(4 5), 773 784.
Zhong, F., Xing, J., Li, X., Liu, X., Fu, Z., Xiong, Z., . . . Jiang, H. (2018). Artificial intelligence in drug design. Science China Life Sciences, 61(10),
1191 1204.